{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "**Chapter 5 – Support Vector Machines**\n",
    "\n",
    "_This notebook contains all the sample code and solutions to the exercices in chapter 5._"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Setup"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "First, let's make sure this notebook works well in both python 2 and 3, import a few common modules, ensure MatplotLib plots figures inline and prepare a function to save the figures:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# To support both python 2 and python 3\n",
    "from __future__ import division, print_function, unicode_literals\n",
    "\n",
    "# Common imports\n",
    "import numpy as np\n",
    "import numpy.random as rnd\n",
    "import os\n",
    "\n",
    "# to make this notebook's output stable across runs\n",
    "rnd.seed(42)\n",
    "\n",
    "# To plot pretty figures\n",
    "%matplotlib inline\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['axes.labelsize'] = 14\n",
    "plt.rcParams['xtick.labelsize'] = 12\n",
    "plt.rcParams['ytick.labelsize'] = 12\n",
    "\n",
    "# Where to save the figures\n",
    "PROJECT_ROOT_DIR = \".\"\n",
    "CHAPTER_ID = \"svm\"\n",
    "\n",
    "def save_fig(fig_id, tight_layout=True):\n",
    "    path = os.path.join(PROJECT_ROOT_DIR, \"images\", CHAPTER_ID, fig_id + \".png\")\n",
    "    print(\"Saving figure\", fig_id)\n",
    "    if tight_layout:\n",
    "        plt.tight_layout()\n",
    "    plt.savefig(path, format='png', dpi=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Large margin classification"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The next few code cells generate the first figures in chapter 5. The first actual code sample comes after:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=inf, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape=None, degree=3, gamma='auto', kernel='linear',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "from sklearn import datasets\n",
    "\n",
    "iris = datasets.load_iris()\n",
    "X = iris[\"data\"][:, (2, 3)]  # petal length, petal width\n",
    "y = iris[\"target\"]\n",
    "\n",
    "setosa_or_versicolor = (y == 0) | (y == 1)\n",
    "X = X[setosa_or_versicolor]\n",
    "y = y[setosa_or_versicolor]\n",
    "\n",
    "# SVM Classifier model\n",
    "svm_clf = SVC(kernel=\"linear\", C=float(\"inf\"))\n",
    "svm_clf.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure large_margin_classification_plot\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA1gAAAC7CAYAAAB4pz8ZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f112fd8e3c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Bad models\n",
    "x0 = np.linspace(0, 5.5, 200)\n",
    "pred_1 = 5*x0 - 20\n",
    "pred_2 = x0 - 1.8\n",
    "pred_3 = 0.1 * x0 + 0.5\n",
    "\n",
    "def plot_svc_decision_boundary(svm_clf, xmin, xmax):\n",
    "    w = svm_clf.coef_[0]\n",
    "    b = svm_clf.intercept_[0]\n",
    "\n",
    "    # At the decision boundary, w0*x0 + w1*x1 + b = 0\n",
    "    # => x1 = -w0/w1 * x0 - b/w1\n",
    "    x0 = np.linspace(xmin, xmax, 200)\n",
    "    decision_boundary = -w[0]/w[1] * x0 - b/w[1]\n",
    "\n",
    "    margin = 1/w[1]\n",
    "    gutter_up = decision_boundary + margin\n",
    "    gutter_down = decision_boundary - margin\n",
    "\n",
    "    svs = svm_clf.support_vectors_\n",
    "    plt.scatter(svs[:, 0], svs[:, 1], s=180, facecolors='#FFAAAA')\n",
    "    plt.plot(x0, decision_boundary, \"k-\", linewidth=2)\n",
    "    plt.plot(x0, gutter_up, \"k--\", linewidth=2)\n",
    "    plt.plot(x0, gutter_down, \"k--\", linewidth=2)\n",
    "\n",
    "plt.figure(figsize=(12,2.7))\n",
    "\n",
    "plt.subplot(121)\n",
    "plt.plot(x0, pred_1, \"g--\", linewidth=2)\n",
    "plt.plot(x0, pred_2, \"m-\", linewidth=2)\n",
    "plt.plot(x0, pred_3, \"r-\", linewidth=2)\n",
    "plt.plot(X[:, 0][y==1], X[:, 1][y==1], \"bs\", label=\"Iris-Versicolor\")\n",
    "plt.plot(X[:, 0][y==0], X[:, 1][y==0], \"yo\", label=\"Iris-Setosa\")\n",
    "plt.xlabel(\"Petal length\", fontsize=14)\n",
    "plt.ylabel(\"Petal width\", fontsize=14)\n",
    "plt.legend(loc=\"upper left\", fontsize=14)\n",
    "plt.axis([0, 5.5, 0, 2])\n",
    "\n",
    "plt.subplot(122)\n",
    "plot_svc_decision_boundary(svm_clf, 0, 5.5)\n",
    "plt.plot(X[:, 0][y==1], X[:, 1][y==1], \"bs\")\n",
    "plt.plot(X[:, 0][y==0], X[:, 1][y==0], \"yo\")\n",
    "plt.xlabel(\"Petal length\", fontsize=14)\n",
    "plt.axis([0, 5.5, 0, 2])\n",
    "\n",
    "save_fig(\"large_margin_classification_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Sensitivity to feature scales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure sensitivity_to_feature_scales_plot\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA1gAAADfCAYAAADrwnJVAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3Xd8VFX6+PHPSYKUQKjSq0AoocWEQIpSBBQbrJW2UlZA\nvwsq6gKCECNlaSoKFvyJDZDgLqKuWBAFNBUSepPeQjeQQIBAkvP7407GTO4dDJLMZJLn7YsX3nuf\n3DnRSc4895zzHKW1RgghhBBCCCHEzfNydwOEEEIIIYQQoqSQBEsIIYQQQgghCokkWEIIIYQQQghR\nSCTBEkIIIYQQQohCIgmWEEIIIYQQQhQSSbCEEEIIIYQQopBIgiVEPkqpj5VSR51c66KUylFKdXd1\nuwpKKbVWKfVzId7vY6XUwcK6nxBCiKKhlOqrlFqnlDqllLqklDqklFqhlLq7iF5P+hshLEiCJYSZ\ntv253vXirLDb92f/PYQQQriZUuoZ4AvgN2AYcC8wBeP3d7cielnpb4Sw4OPuBgjhgZS7GyCEEELk\n8wLwhdZ6RJ5za4GF7mmOEKWXjGAJcRNs0y8WKaUeV0rtVEpdVEptUEqF54vrqJRapZQ6q5TKUErt\nV0rNzxfT2HavE0qpK7aYN/JcD1ZK/UcpddQ29WO3UmqaUqpcAdpZXSn1rlLqmO3eu5RSwy3i7lJK\nJSulLiul9iqlRljdTwghRLFTDThVkEDpb4QoWjKCJcTN0cAdgD8wEcgEpgL/U0o11lqnK6V8ge+B\nBOAJ4CLQGAjLvYlSqjGwwXZtErAPaAD0yvNaDYFNwIe2uABgMtAEGOCsgUqpSkAcUNYWfwi4G3hX\nKXWL1vptW1wrYCWwHngMKAdEARWBrBv+LyOEEMKV1gNDbGuYvtJa77UKkv5GiKKntJaprkLkpZT6\nCLhLa93Q4loXYA3QQ2v9s60j8wOaaK3TbTFBGJ3XAK11tO14PdBea73dyWt+CvQFmmutC/oE0hvo\nB3wC3Kq1Pmc7vwbQWuvutuNJwEtAG631gTxf/77tNWtrrXOUUkuAnkBDrfUVW0x9YD+QorW+rSDt\nEkII4XpKqebAf4C2GFPZfwd+BD7SWv+YJ076GyGKmEwRFOLmxecmVzbbbH/nJmh7gfPA+0qpgbZO\nJL+ewDfX6+yUUpWUUjOVUvuUUpnANWARRkfa/DrtuxtIBA4rpbxz/wCrgBpAa1tcZ+Db3M4OQGt9\nDIi9zr2FEEIUA7YRq0CgC8ZMik0YSc0PSqmJeUKlvxGiiMkUQSHMsgBvJ9e888TkSs0boLW+qpQC\nY8oDtmmC3TCmYrwN+CmldgCRWusvbF9WHTj2J+36GOhuu88WIAPoBMzPfS0nagJNMTrI/LTttQHq\nYD1//xTGlEYhhBDFmDamJcXY/qCUqg38AExWSs3XWqch/Y0QRU4SLCHMTgM1lFI+Wuv8c8HrYnQS\nBZpWkUtrvRV4VCnlBQRjTKH4XCnVTmu9EzgL1HP29UqpssCDwGSt9fw859sX4OV/t7X3GawrIP5m\n+/sEUMviutU5IYQQxZzW+qRS6gNgLsbIUxLS3whR5GSKoBBmazAePjxoce0R4ITW+jeLa39Ka52j\ntV6PsfjXC2hlu7QKuF8p5axzKYsxepY/4RtSgJf9HmgJHNVab7T4k2GLiwfuVUqVz/1CpVQDINzi\nnkIIIYoR22iVldx+5qTtb+lvhChiMoIlRD5a69VKqdXAx7ZKR4lAJaA/8AAF62TslFL3ASOAL4GD\nGFWSngHSMToZgEiMTSHjlVLTMao61Qfu1lr/3TbNMAF4QSl1EuMJ5DCMaRZ/5g2MKk0xtjK8vwG+\nGJ3gHVrrvra4qcCjwI9KqdkYnewr/NEpCyGEKL622/qubzH6Gj/gPmAksMy2xgmkvxGiyEmCJYS1\nB4CXgb/b/r4KbAb6aK2/yRPnbNf5vOf3Apds96kDXMCoMthTa30cQGt9WCnVCaPTmY6R0KVgJGW5\n+gHvYsyBvwwswyihm7c9eV8f273TlVJhGKNmYzGmhpzH6PiW54nbrZTqDcwGom2vPxMIBbpa/lcS\nQghRXEzASJyiMKbaZQN7MH7vv5kbJP2NEEXP5WXalVItMRb6B2GsdRmrtf7Sdu0ujB/mBhijBkO1\n1kdc2kAhhBAlllLqFuAdoAdQFePp/USt9fdO4sdgfFAsh/EB8WmttdUCfiGEEAJw8RosW6nOr4Cv\nMTq2kcBipVQzpVR1jM5rIsZu5MkYT0yEEEKIwuIDHMGYrlQZ40n750opq33v7sZIrrphVDZrijE6\nIIQQQjjl0hEspVQAxp5BfnnO/QAkYJQMHay1jrCdr4Ax77eD1nqPyxophBCiVFFKbQFe0VqvyHd+\nCXBQa/2y7bg7sERrXZC1KEIIIUopV1cRtCrZqYA2QADGXgsAaK0vYezoHeCapgkhhChtbJXUmgM7\nLC479Eu2f6+plKrqirYJIYTwTK5OsHYDp5VSLyqlfJRSvTB2HK+AUVktLV98GsbiSyGEEKJQKaV8\ngMXAx05mSuTvl9IwHgpKvySEEMIpl1YR1FpnKaX6YhSyGIex4d0yIBO4iFFSNC8/jIprDpRSrq3M\nIYQQoshpra1mORQJpZTCSK4ygdFOwvL3S34YFdOkXxJCiBLuZvokl280rLXerrXuqrW+VWvdG2PR\n8HpgJ9AhN04p5Wu7ZjVtA611qfwTGRnp9jbI9y3ft3zv8n0X9h83WAjUAB7SWmc7idkBtM9z3AE4\npbU+ZxWstebw4cN89tln/POf/yQ5Odnp/+OhQ4fywQcfsHv3bnJycuT9Jm33iD+e2m5pu7T7Rv/c\nLJfvg6WUaouxL4M38H9AbeBjoDIwSyn1N4xN8iYDW7QUuBBCCFGIlFLvYWx82kNrffU6oZ8CHyml\nPsPYAHUi8NH17t2wYUMaNmxI//79ncYsW7aM3bt389FHxq1q1KhBWFgYc+bMoXnz5jf43QghhChu\nXD6ChbFx6wmMzqobxmar17TWZ4GHMTa9SwU6Ymx0J4QQQhQKWzn2EdhGo5RSF5RS6Uqp/kqpBrZ/\nrw+gtf4BmAWsAQ7a/rxys21YvHgxc+fO5ZFHHqFOnTqcPXuWr7/+mkqVrJd2Xbx48WZfUgghhAu5\nfARLaz0WY18Rq2s/A61c2yLP0rVrV3c3wS3k+y59Suv3Xlq/b1fRxub113u46LAWWGs9F5hbmG0I\nCgoiKCiIZ599Fq01hw4dIjk5mdq1a5tir169Sq1atWjUqBERERGEh4cTERHBbbfdhrGM7OZ48vtN\n2u56ntpukLa7g6e2uzC4dB+swqKU0p7YbiGEENaUUmgXFrkobEXVL+3cuZPbb7+dzMxMh/MtW7Zk\n586dhZJkCSGEcHSzfZLLR7CEEEIIUTCtW7cmLS2NjRs3EhsbS0xMDLGxsTRp0sQyuTpz5gxJSUmE\nhoZSpUoVN7RYCCGEjGAJIYRwOxnBKjitNenp6VSuXNl0bdGiRTzxxBMopWjTpo19SmGXLl2oX7++\nS9onhBCe7mb7JHcUuRBCCCHEX6SUskyuAHx9fQkNDcXHx4dt27bx3nvvMWjQIGbNmuXiVgohROkl\nI1hCCCHcTkawCtfly5dJSkoiNjaW2NhYRowYwQMPPGCKW7RoEfv37yciIoJOnTo5rWQohBClyc32\nSZJgCSGEcDtJsNzjrrvu4ueffwbAy8uLDh06EB4ezujRo2VPLiFEqeVxUwSVUo2UUiuVUqlKqeNK\nqXlKKS/btQ5KqSSlVIZSaoNSqr2r2yeEEEKUFmPGjGHMmDF07NgRLy8vNm7cyLx588jIyLCM98Qk\nUgghXM3lI1hKqZXAKWAkUBVYDbwPLAD2Aq8D7wJPAS8AzbTWWfnu4ZFPCoUQQliTESz3y8jIYP36\n9cTHxzNu3Di8vb1NMe3ataNevXr2PblCQkKoUKGCG1orhBBFx+OmCCqldgLPa62/tx3PAioBXwAf\naq0b5Ik9DAzXWq/Kdw+P78iEEEL8QRKs4u/YsWM0aNDA4ZyPjw/BwcH8+uuv+PjIzi9CiJLB46YI\nAnOB/kqp8kqpekBv4HsgANiaL3ar7bwQpcrBg4cZNCiKbt0iGTQoioMHD7u7SUKUCEqpf9qmoF9R\nSn14nbjBSqkspVS6UuqC7e87XdnW4qZ+/focOXKEpUuXMmrUKAIDA8nJyeHy5cuWydXly5fZuXMn\nOTk5bmitEEK4jzseN/0CjADSMRK8T7TWXymlXgbS8sWmYYxuCVFqHDx4mJ4957F/fxTgC2SQkBDJ\njz+OpkmTRu5unhCeLgWYAtwNlP+T2DitdalOqvJr0KAB/fr1o1+/fgCkp6dz/Phxy9i4uDh69OhB\ntWrVCAsLs+/JFRwcTLly5VzZbCGEcCmXjmApY9v5H4D/AhWAGkA1pdRM4CLgl+9L/IALrmyjEO42\nadLHeZIrAF/2749i0qSP3dgqIUoGrfWXWuuvgVR3t6Uk8PPzo2XLlpbXzp07R926dUlNTeWbb77h\npZde4o477mDw4MEubqUQQriWq0ewqgH1gbe11teAc0qpjzCeJj6PUdQir3bAfKsbvfLKK/Z/79q1\nK127di2C5grheikpOfyRXOXy5fhxmWYjSo61a9eydu1adzfjzwQqpU5jJGOLgelaa/lBLKBHHnmE\nhx9+mMOHDxMTE2PfkysiIsIyfu3atRw8eJCIiAiaNWuG8UxWCCE8jzuKXOzDqBr4Gsb0vw8xRq/+\nAezBqCK4AGMa4QtAc6kiKEqTQYOiWLLkRRyTrAwGDpzD4sWR7mqWEEXK1UUulFJTgHpa62FOrjcG\ntNb6sFIqAPgc+FRrPdNJvPRLBaS1tkyeBg0axJIlSwCoWbMm4eHhhIeH8/DDD9O4cWMXt1IIUZrd\nbJ/kjjVYDwFvAuOBLGANRlXBa0qpvsBCYAawC+iTP7kSoqSbMmUICQmRDmuwmjaNZMqU0W5umRCl\nh9b6UJ5/36GUehV4EbBMsEBmVhSUs5GpHj16cPnyZWJiYjh9+jQrVqxgxYoVtGzZUhIsIUSRKuxZ\nFS4fwSoM8qRQlHQHDx5m0qSPOX48h7p1vZgyZYgUuBAlWnEbwbKIfxz4l9Y62Ml16ZcKidaa/fv3\nExsbS0xMDLNmzaJq1aqmuL///e/4+vra9+Rq3LixTCsUQhQKj9sHqzBIRyaEECWLqxIspZQ3UAaY\njLEmeDiQpbXOzhd3D7BRa31aKdUS+A+wTGs91cl9pV9yoUuXLlG5cmWysv6Y5FKnTh0iIiJYuHAh\nlSpJAWIhxF8nCZYQQgiP58IEKxKIBPJ2IlHAR8BOoJXW+phSajbwd4x5uqeARcDU/IlYnvtKv+RC\nWVlZrF+/npiYGHsBjdTUVG699VZOnTplGsnKycnh4sWL+PnlL1YshBBmkmAJIYTweK6eIljYpF9y\nr5ycHH777TeOHTtGz549Tdd37txJ27ZtadeunX0/rvDwcBo0aOCG1gohijtJsIQQQng8SbBEUVqx\nYgWPPfaYw5RCgL/97W988cUXbmqVEKK48sQqgkIIIYQQLvO3v/2NtLQ0NmzYYC+eERcXR/PmzS3j\nd+/ezfHjx+nUqRO+vvn3JXS/3EJIKSk51KsnhZCEKG5kBEsIIYTbyQiWcLWcnBwuX75smUCNHTuW\n2bNn4+3tTWBgoH1KYdeuXalRo4YbWvuHgwcP07PnPNNWHj/+OFqSLCEKyc32SV6F2RghhBBCCE/g\n5eXldHSqfv36BAUFAZCUlMTcuXN59NFH+fLLL13ZREuTJn2cJ7kC8GX//igmTfrYja0SQuTl0imC\nSqkL/FG5SQHlgbe11s/art8FzAcaAInAUK31EVe2UQghhBCl2zPPPMMzzzzDxYsXSUxMtE8rvOOO\nOyzjp02bhpeXFxEREQQHB1O+fPkia1tKSg5/JFe5fDl+PKfIXlMIcWNcmmBpre0bUyilKgAngc9t\nx9WB5cAw4BtgKrAMCHVlG4UQQgghACpWrMhdd93FXXfd5TRGa82bb77JmTNnAChTpgxBQUFEREQw\nfvx4qlevXqhtqlfPC8jAMcnKoG5dmZQkRHHhtjVYSqnBwCStdTPb8XBgsNY6wnZcATgLdNBa78n3\ntTLXXQghShBZgyU8VVZWFv/5z3/s+3Ft3boVrTU+Pj6kpaVRoUIF09dorU17dRWUrMESouh5bJl2\npdRPwDqt9au247lAGa31P/PEbAMma61X5Pta6ciEEKIEkQRLlBRpaWkkJCRw4MABnn76adP11NRU\nWrVqRWhoKOHh4YSHhxMUFETZsmUL/Bq5VQSPH8+hbl2pIihEYfPIBEsp1RDYDzTTWh+2nfsAOK21\nnpAnLgZ4X2v9ab6vl45MCCFKEFclWEqpfwJDgLbAZ1rrYdeJHQOMBcphTGF/Wmt9zUms9EuiQL7/\n/nt69+7tcK5s2bI89NBDfPbZZ25qlRAiL0+tIvgEEJObXNlcBPzyxfkBF1zWKiGEECVdCjAFWHi9\nIKXU3RjJVTegMdAUiLre1yxdupSjR48WTitFiXX33Xezb98+PvnkE4YPH07r1q3JzMzEx8d6WfyZ\nM2fYv38/ksAL4TncNYL1GzBda/1JnnP512D5AqeBQKs1WJGRkfbjrl270rVrV1c0XQghRCFYu3Yt\na9eutR9HRUW5dIqgUmoKUM/ZCJZSaglwUGv9su24O7BEa13HSby9M23QoIF936Tw8HDatm2Lt7d3\nEXwXoqRITU3l4sWLNGzY0HTtjTfe4Pnnn6dWrVqEh4fb31uBgYGUKVPGDa0VouTzuCmCSqkw4Aeg\nttY6I8/5GsBejCqC3wKvAndorcMs7iFTMYQQogRx9RqsAiRYm4FpWuv/2I6rYzz0q6G1PmcRr3v3\n7k1cXBxpaWkO1ypVqkRoaKj9g3GnTp2c7r8kRH6zZs1i9uzZnD171uH8q6++yqRJk9zUKiFKNk9M\nsN4Dymmth1hc6w68DTTE2AdriNU+WJJgCSFEyVIME6x9wP9prVfZjn2Aq0Dj6/VLOTk57Nixw75v\nUmxsLIcOHXKI9fb2JjAw0GE0ok4dy4ExIQCj6uCePXuIjY21v7feffddunfvbopdtmwZ2dnZRERE\nWI6ICSH+nMclWIVBEiwhhChZimGCtRmYqrX+r+24GnCG64xgOeuXUlJSHD4Yb968mZwcx01hmzRp\nYk+2IiIiaNWqFV5esq+RuHHt27dn69atANSvX9/+nurfv3+h78klREklCZYQQgiPVwwTrCXAAa31\nJNtxd2Cx1rquk/gCrw2+ePEiiYmJ9hGu+Ph4Ll686BBTpUoVwsLC7ElXx44dKV++/F/4TkVporVm\n9uzZ/PLLL8TFxXHu3B/PAo4cOUKDBg3c2Dohiq/CXhcsCZYQQgi3c2GZdm+gDDAZqA8MB7K01tn5\n4u4GPgLuAk4C/wUStNYTndz3L/dLWVlZbNu2zT7CFRMTQ0pKikNMmTJlCAoKcphWeOutt/6l1xOl\nQ05ODrt27SI2Npbt27fz1ltvmWKysrLo1q0bt99+u70oS7169dzQWiGKFxnBEkII4fFcmGBFApFA\n3k4kCiOZ2gm00lofs8U+B4zH2Afrv7hoHyytNUeOHHGYVrht2zZTme7mzZs7TCv09/dHKY/dq1m4\nwcaNGwkKCnI417hxY+6++27ee+89N7VKCPeTBEsIIYTHc/UUwcJW1P1SWloa8fHx9qQrISGBy5cv\nO8TUqFHDPgoRHh5OUFAQZcuWLbI2Cc93+fJlh/dVXFwcFy5coHv37vz000+m+CtXrpCTk0OFChXc\n0FohXEcSLCGEEB5PEqwbc+3aNTZv3mxfxxUbG8vJkycdYsqWLUvHjh3tI1xhYWFUq1bNZW0Unic7\nO5vt27eTmZlJSEiI6fqyZcsYNGiQfUph7ghqrVq13NBaIYqOJFhCCOHhLl26RHR0ND+vWkV6Whp+\nlSvTvVcv+vXrV2qeFEuCdXO01hw4cMBhWuHOnTtNca1bt7aPcEVERHDbbbfJtEJRYDNmzGDixImm\nKpgTJkxg2rRpbmqVEIVPEiwhhPBQ2dnZTImKYv68eYT6+/NwcDBVfX05l5HB8qQk4vfsYdTo0UyK\njMTb29vdzS1SkmAVvtTUVOLi4uwJ14YNG8jMzHSIqVWrlsM6rg4dOlCmTBk3tVh4gvT0dBISEhym\nqy5YsICBAweaYuPj48nJySE4OFimqwqP4pEJllKqH0YFp4bACYwNhWOVUncB84EGGBsND3W2oeOZ\nM2fw9vbG29sbLy8vvL29ueWWW0r8hxAhRMmQnZ3NgMcf5/TevXw4YgRNatY0xRw8fZph779PLX9/\nlkRHl+jfb5JgFb3MzEw2btzoMK3w7NmzDjEVKlQgJCTEnnSFhoZSuXJlN7VYeIKsrCyys7MtE6h7\n7rmHH374gbJlyxIcHGwfPe3WrRuVKlVyQ2uFKBiPS7CUUj2B94HHtNYblFK529dfBfYDw4BvgKnA\nHVrrUIt7WDZ62rRpTJgwwXT+lVde4bXXXjMlZBMmTOCZZ54xxb/11lssXLjQIdbb25unnnqKJ554\nwhS/aNEili9fbop/7LHH6NOnjyn+m2++4aeffjLF9+rVizvvvNMUHxsbS3Jysik+ODiY9u3bm+J3\n7drFvn37TPFNmzalUaNGpviTJ09y9uxZU3y1atUsO9bMzEyysrIc4r28vGSaiRA34JXJk1n31Vd8\nP24cZa8zYpB57Rr3zJxJlz59eOXVV13YQteSBMv1tNbs2bPHPsIVGxvLnj17HGKUUrRt29ZhvU3D\nhg3l970okHHjxvHdd9+xfft2hyqYCQkJdOrUyY0tE+L6brZP8inMxhTQK8CrWusNAFrrEwBKqeHA\ndq31F7bjV4CzSil/rfWe/DepVq0a2dnZZGdnk5OTQ3Z2Nj4+1t/OlStXTJs4grHuwUpKSop9F/S8\nHnzwQcv47du389VXX5nOt2nTxjLBiomJYe7cuabzFStWtEyw/ve//zFz5kzT+enTp1smWJ988onT\n+Jdeesl0fu7cuTcUHxkZeUPxU6dO5a233jIluGPHjuXpp582xb/99tt8+umnpvjhw4fTv39/U/xn\nn33G119/bYp/5JFH6N27tyn++++/Z926dab47t27ExYWZopPTExky5YtpvjAwEBat25tit+zZw+H\nDx82xTdq1Mhyf5GzZ89y/vx5U7yfnx++vr6m+OxsY7seSWo916VLl5g/bx5JU6fak6uDp84waVkS\nKefKUq9qJlMeD6ZJrVspW6YMH44YQcdJkxg7fnypWZMlip5SihYtWtCiRQuGDTP2Wz59+rTDtMLk\n5GS2bt3K1q1beffddwGoV6+ew7TCtm3bOu1/Rek2c+ZMZs6cyfnz54mPjycmJob169cTGBhoGf/0\n00/TrFkzIiIiCAwM5JZbbnFxi4UoHC79jaiU8gKCga+VUnuBssCXwFggANiSG6u1vqSU2m87b0qw\nfv/99wK/blRUFBMmTDAlZBUrVrSMHzNmDAMGDDDFN2zY0DL+iSeeoHPnzva43K+xSn4A7r//fmrW\nrGmKv+OOOyzjw8LCGDVqVIHv37JlS+677z5TfOPGjS3ja9asSevWrU3xVatWtYz38fGhfPnyDvFa\na7y8vCzj09PTOXPmjOV5K4cPH2b9+vWm83fffbdl/ObNm1m2bJnpvL+/v2WCtWbNGmbNmmU6X6ZM\nGcsEa/ny5cyePdt0fsaMGZYJ1sKFCy3v/+9//5vx48ebzs+ePdsyfsaMGYwbN850fsKECfZ4Ly8v\ne0I2bdo0XnjhBVP89OnTee+990wJ3Isvvsg//vEPU/yCBQtYunSpaURz6NChPPLII6b4zz//nO++\n+84U36dPH3r27GmKX716NXFxcab4O++8k44dO5rik5OT2blzpym+TZs2+Pv7m+IPHjxISkqKKb5e\nvXrUtJiGd/78eTIyMkzx5cuXL7I1A9HR0YT6+9PY1p6Dp87Qc+pW9p+aB/gCGSTsfYEfX25Hk1q3\n0qRmTTo3b050dLT9g7AQRaFmzZr07duXvn37AkYZ7w0bNtinFMbGxpKSksKyZcvsv3crVqxI586d\n7UlX586dnfavonSqUqUKvXv3tuyTc508edJh761y5crRqVMnIiIiePXVV51+xhCiOHL1I6daQBng\nYSAcyAK+Bl4GKgKn88WnATc9Sbds2bI39EGpdu3a1K5du8DxAQEBBAQEFDg+IiKCiIiIAsc/+OCD\nTkfPrAwZMoQhQ4YUOP7555/n+eefL3D81KlTmTp1qsO5602NmTx5Mi+88EKBE7hRo0bx0EMPmRLc\npk2bWsYPHDiQwMBAU7zVh3Uw5oRXrlzZFB8aapqNCkBISAhPPvmkKd4quQJo1qwZd911lym+fv36\nlvHVqlXjtttuK/ADALAPXZOTk0NOTg5ZWVmmqk65zp07x9GjR03nU1NTLeP37dvHunXrTOe7du1q\nGb9hwwY+/vhj0/mGDRtaJlg//PADc+bMMZ2fOXOm5f+z6Ohop/Fjx441nX/nnXduKH769OmWCbSz\n+PHjx/P666+bErKoqCjLKcezZs2yTznO/XPk8GEevv12e8ykZUl5kqsPgP+y/xTcEfktHZvWxksp\nbqtWjZ9XrTIlWMuXL2f16tUO9/fy8uL++++3/H+2Zs0aNmzYYIoPDw+nQ4cOpvgtW7awZ88eU3yr\nVq1o0qSJKf7o0aOcOnXKFF+rVi3LEuEXL17k6tWrpvOieChfvjx33nmnfXZFTk4Ou3btcphWeODA\nAVavXs3q1asB8Pb2pn379g7TCq1G74XIq0KFCnz44Yf2RH737t2sW7eO06dPmz5zwB+fO2QmhyiO\nXJ1g5e6K+JbW+jSAUup1jARrHeCXL94PuGB1o1deecX+7127dnX64U+4xvV+wVWsWPGGnmY2bNjQ\n6Wihlfbt2zsdzbPSrVs3unXrVuD4Rx55xHLkxpnhw4czfPjwAsePGzfOcqTKmdwpF1rrAk2RnThx\nIqNGjTIlcFajOQBPPfUU9957rykhbtmypWX8Y489RqtWrUzxVqOBAD169KBcuXKmeGcJ8e23387A\ngQNN8VbuZ/JLAAAgAElEQVSjVwCNGzcmLCzMFO9sn5ZKlSpRp04dU3y5cuUs469du2b/k5ezJOHU\nqVOmdS0AWbapngAp58piJFcAu4AfjPOpkJJqJMeDu3ThgsXPWXx8vMNT31y1a9e2/L24cuVKXnvt\nNdP52bNnWyZYixYtsoyfNWsW//rXv0zn33zzzQLFr127lrVr17Jq1Sri4+NN8aJ48vLysj9UHDFi\nBAAnTpxwKA+/adMmNm7cyMaNG5k3bx5g/FzmLQ8fEBAgIxLCgZ+fH0OHDmXo0KGAMX0+Li7O6e/W\nX3/9lf79+zu8r9q3by/TVUWx4I4iF0eACVrrxbbjh4CJwLsY1QQjbOd9MUa0AvOvwfLExcRCiJIh\nOzvbPmKYPyErX768Kf7MmTP8/vvvDrEvjR3LPfXq8ey99wIw6K3vWBKTO4K1GzgIZHBnq3d57r7b\nyc7JYc/x4+zUmsXR0Q73j4uLY/PmzaYEukuXLpYbhX7zzTesW7fOFP/QQw/Ro0cPU/ynn37K119/\nbYp/8sknefjhh03xb7zxBosXLzbFjx071nJ646RJk3j77bc5d+6cy4pcKKWqAh8CPYEzGH3SUou4\nSIz+6QqgAA2001ofsoiVfskmIyODxMREe9IVFxfHhQuOz0orV65MWFiY/cNxSEiIrC8UN2TOnDmm\nhzy+vr6MGTOGKVOmuKlVoqTwxCqCUcA9wP0YUwS/An7GKM++F6OK4LfAqxhVBE2PwaUjE0J4sg8/\n/JAVCxbwvxdfBPKuwXqN3DVYTWv9sQYL4P45c3ho5MgSuwbLlVUElVK5ydQw4HZgJRCqtd6VLy4S\naKq1NpePNd9T+iUnsrOz2b59u0N5+CNHHHdg8fHx4fbbb7dPKQwPD3c66iwEGNNVf/vtN4fpqvv2\n7XM6vfvgwYOUKVPG6XR9IfLyxATLB3gTGIAxZXAZME5rfVUp1R14G2N/rESMES3LfbCkIxNCeKpL\nly7RsF49Nkydat//KreK4PFzt1C36lV7FUEw9sPqOGkSR44dK7FP+V2VYCmlKgDngNZa6/22c58C\nx7TWE/LFSoJVRI4ePerwwXjr1q2mdaTNmjVzmP7VsmVLWW8jrit3/WeNGjVM14YNG8ZHH31Ew4YN\nHapgBgQElOg9BsVf43EJVmGQjkwI4eluZB+su2fMoGvfvrIPVuG8TgcgVmvtm+fcC8CdWus++WIj\ngeeAbOAE8LbW2rzgDemXblZ6ejoJCQn2pCsxMZGMjAyHmGrVqtkTrvDwcIKDg52ulRQiv6effpql\nS5eSlpbmcH7FihX2qplC5Cq1CdaiRYtMVarccSxP04QQf0V2djYDHn+c03v38uGIEfaRrLwOnj7N\n0AULqN2iBUuio0v0U1YXJlgRwOda67p5zj0JDNBad88X2xI4D5wCOgPLgTFaa9O+EJJgFa6srCy2\nbNliH+GKiYnhxIkTDjG33HILwcHB9tGIsLAwy5ELIXLl5OSwY8cOh6IsiYmJlkWf3nrrLerWrUt4\neDh16tQp1HZcunSJ6Ohofl61ivS0NPwqV6Z7r17069evxM5S8DSlNsFydxtyKaVcktC5M4ksLse5\n5ySpFSVFdnY2U6KimD9/Pp2bN+fhoCCq+vpyLiOD5cnJJOzdy+jRo3l58uQSnVyBy0ewYrTWFfOc\nex7okn8Ey+JrxwHBWutHLa5JglWEtNYcOnTIYVrhjh07TFuEtGzZ0qE8fLNmzaTPEDcsMzOTypUr\nk5mZCcBtt91mf18NGTLkL2+AbP+dP28eof7+PBwc/Mfv/KQk4vfsYdTo0UyKjCzxv/OLu1KbYFlt\nBOzqY2f7DomilZvUFoekr7Qk1vIBpWjlfZp5IT2dSn5+pe5ppovXYKUCAXnWYH0CpORfg2XxtWOB\nEK21ad8GpZSOjIy0H8v2IUXv3LlzxMfH25Ou9evXc+XKFYeYmjVrOqzjCgwM/MsfjkXpkZ6ezhtv\nvEFMTAwJCQlcvHgRMDZM/v333//SFgMFnbUw7P33qeXvX+JnLRQ3uVuH5IqKiiqdCVZxaHfejV7z\nJmDuTPqcHReHNhRGm4rD//fSyJVJbXFJKt19XNqSWhdXEfwMo+T6cCAQ+AYIs6gi+CDwi9b6vFIq\nBPgCGJ+7zUi+2GLRL5VmV69eZdOmTQ7TCs+cOeMQU65cOUJCQhymFVapUsVNLRaeICsri61btxIb\nG8ulS5cs963cv38/gwYNcqiCeeuttzrE3Mi623tmzqRLnz4let1tcVdqR7A8sd3i5uUmtcUp6SuO\niWhhHsvPmnvkTWqLS9JXlMeDBg1y1z5YZzEq2S6zrc/6VmvtZ4v7DOgF3AIcwyhy8baTe0q/VMxo\nrdm3b59Defjdu3c7xCilCAgIcJhW2Lhx41L3gEPcnE8++YQhQ4Y4nPP392fw4MFMmDDBXjk2aepU\nGuerHJtyriz1qmaWusqxxZ0kWKWALIYU7pQ/qS0OSV9xTEQLs02l6fdbXq5KsIpCaeuXPNXZs2eJ\ni4uzj3AlJSVx9epVh5jcwga5SVf79u3x8fFxU4uFJ0hPT3d4XyUmJnL58mWee+453njjDdPeh1sO\nHeGhObs5cHoupXXvw+LOZQmWUup2YBDGtIpGGFMrRgJVgHrAZK31wb/akBtRWjqy7GxZDClEaaS1\nLnZJX1EnotHR0ZJgCZe7cuUKSUlJ9g/GcXFxpKamOsT4+vrSuXNne8LVuXNnKlWq5KYWC09w7do1\nNm/eTOXKlfH392dQv370qF6dIbZ1mW2en8qOY78BIUA4EAG0Z2DEZBY/0xuAj9as4afUVBZHR7vp\nuyjdXJJgKaWaAaO11s/ajj8CwoDBgBfwK/Ci1vqNAtxrLdAJuAYojM0dW9muDQCmA9WBH4FhWuvz\nFvco8R1ZdrYshhRClB6uXINVFEpDv1Qa5OTk8NtvvzlMK9y3b59DjJeXF+3atXNYb9OgQQM3tVh4\nggd79+YfbdvSp2NHAGqPeJlT5/eY4lrV68bON54G4Mv16/loxw6++vZbl7ZVGG62TypoGZTngPF5\njn2BVK11AnAEeA34pID30sD/aa39tNaV8iRXAcB7wECgFnAZeLeA9yxxpkRFcXrvXr4fN84yuQJo\nUrMm348bx6k9e5gSFeXiFgohhBAli5eXF61atWL48OF8/PHH7N27lxMnTrB8+XLGjBlDSEgIXl5e\nbN68mfnz59O/f38aNmxIo0aNGDBgAO+88w5btmwhOzvb3d+KKEb8KlfmXJ6Ns3u0Ccf4+Pw/jI/X\nEUBZmtT0s8ecy8igkp9xvHLlSjZs2MC1a9dc2WxxEwo6gtVAa300z/Ex4COt9SQn8RWBj4HntNbH\n8l1bAyzSWn+Y7/w0oJHWepDt+DZgF1BNa52RL7ZEPynMvxgycsHXnD9u/mVdpa43USMflMWQQgiP\nJyNYwlNcunSJ9evX26cVxsfHk5aW5hDj5+dHaGiofYSrU6dO+Pr6uqnFwt3yr8E6eOoMPaduZf+p\n18hdg3VbzedYNbE9TevUAv5YgzV06FDq16/P8ePHqVChAp06dbJPV+3WrZtsO1BEXF7kwraz/U6g\nh9b6Z4vr/wAaAJOAJlrrI/murwFaY0wP/A14WWu9Tin1JRCrtZ6dJ/YCcKfWelO+e+iBAweaqlH1\n7duX3r17m9r8448/8ssvv5jiu3TpQmhoqCk+KSmJrVu3muLbtWtHq1atTPH79+/nyJEjpvgGDRpY\n7v6dmppKenq6Kb5ixYqUL1/e9IP4zOQveGj3SNN9VrR6nzej/gbIYkghhGeTBEt4quzsbHbs2GGf\nUhgTE8Phw4cdYry9vQkMDHSYVmj1+UCUTLkPzjdMnWqflZRbRfD4uVuoW/Wq0yqCXl5e/N///R8x\nMTHs3bvXfk8vLy/Onz8v6wGLyM32SX+lLE4PIBOIy9OIJrkFLrTWC23nJjv5+rEYCdpVoD/wtVIq\nEKgIpOWLTQMs3zlLliwxnWvcuLFlgvXzzz8zY8YM0/np06dbJlj//e9/mTlzpun8tGnTLBOsDz74\nwPL+06ZNY8IE876Vs2fPdtqel156iZ9XreLh4GD7+bgzW5hHN7zwwhtvFAovvAj+vSVgJFgPBwXx\n06pVDBs2jNmzZ/Pee++ZSiE/++yz/OMf/7Bsf7RtDVfe+L///e888ohpP02++OILvvvuO1P8/fff\nT48ePUzxa9asIT4+3hQfHh5OcJ7vM9fmzZvZtWuXKb5Vq1Y0b97cFH/48GFOnDhhiq9du7ZpHwqA\nCxcucOnSJVN82bJlKXOdvSmEEEKI/HIfwLZr146nnzbWz6SkpNiTrdjYWDZv3kxSUhJJSUnMnTsX\ngNtuu82hPHyrVq3+0ga2ovirUKECo0aPZtj779v3wWpS61Z7QYu8Mq9dY+iCBYwaNco+K+nDD41J\nX6dPnyYuLo6YmBjOnj1rmVxduHCBESNG2BP5du3ayRp9N/jTBEspVQ6IAj7VWu/ASLC2aq2v2K4r\n4EXgnwV5Qa31hjyHnyql+gH3AhcBv3zhfsAFq/v07dvXXj7a39+fZs2aERISYvmaPXv2pHz58qYK\nVp07d7aMDwoKYsiQIab41q1bW8Y3adKELl26mOLr1q1rGV+5cmUaNmxois/9QUpPS6Nqw4amr8ux\n/WM/zvO0tKqvLxcOHQKMMrQHDhwwff3Zs2ct27Nnzx5++ukn0/mwsDDL+MTERD744APT+dq1a1sm\nWN9//z2zZs0ynf/3v/9tmWAtXbrUafz48eNN5995550bip82bZplAu0s/uWXX+b11183JWSTJk3i\nmWeeMcW//vrrLFy40DRCOWrUKJ544glT/EcffcTy5ctN8QMHDuTBBx80xX/11VesXr3aFN+7d2+6\ndOliiv/ll19ISkoyxXfq1IkOHTqY4rdv387evXtN8f7+/jRu3NgUn5KSwpkzZ0zxNWrUoGrVqqb4\ny5cvc/XqVcv9kGTvmdJj7dq1rF271t3NEKJI1KtXj8cee4zHHnsMMD70JiYm2pOuhIQEDhw4wIED\nB1i0aBEAVatWJSwszJ50BQcHU758eXd+G6IQTYqMZNfOndwzc+Z1i5cNXbCA2i1aMCky0nS9Zs2a\n9O3bl759+zp9nYSEBKKjo4m2VR+sVKkSnTt35oEHHmD06NGF9w2J6yrICNa9GAlUslIqC7gNyFvZ\nbyLwaSG0ZTtg/7RnW4N1C2AuswKsWLGiwDfu3r073bt3L3D8o48+yqOPPlrg+BEjRjBixIgCx48f\nP97yg3yu/Ishw2t2YNbv8+0JVu4/K6v9UVck72LIcePGMWLECFMCV6tWLcvXGz58OD179jRt4Gs1\nWgfw8MMP07RpU1N8eHi4ZXy3bt3w8vIyxVslVwDt27fn8ccfN8VbjV4BNGjQgJCQEFO81egVGCV3\nb731VlN82bJlLeMzMzO5fPmy6fyVK1cs40+cOMHOnTtN50+ePGkZv3PnTlauXGk6HxQUZJlgxcbG\nMn/+fNP5atWqWSZY33zzDbNnzzadnzFjhmWC9emnnzqNt9rB/s0337yh+MjISMt4ZwluZGQk8+bN\nM21SO3HiRJ566inL9ixatMiUvD311FP079/fFL9o0SK+/vprU/xjjz3Gvffea4pfuXIl69atM8X3\n6NHD8mcgPj6ezZs3m+KDgoIICAgwxe/evZtDhw6Z4ps0aUL9+vVN8adPn+bcuXOm+MqVK1s+3bx2\n7Rpaa3u8u5Larl270tVWshggqgQU6pk4cSLh4eGEhYVRpUoVdzdHFCOVKlWiR48e9oeQWVlZbNu2\nzT7CFRMTQ0pKCitXrrT3B2XKlCEoKMhhWqGzfk0Uf97e3ny2bBlToqLoOGkSnZs35+GgoD+230lO\nJmHvXkaPHs3Lkyf/5VGnNm3a8N5779nfVwcPHuTHH3+katWqlglWTk6OjJwWgYIkWOuAj4AgIBij\nxPo7Sql3Mab5fa21TizIiymlKtu+fh2QBfQD7gCeBcoAcUqpcGAzxqjZ8vwFLkqD7r16sXzBAvt+\nCQBetn/y8vH644dveXIyD4001mlVq1aNatWqFfj1mjdv7jR5sRISEuJ0tNDKPffcwz333FPg+AED\nBjBgwIACx48aNYpRo0YVOH7SpElMmmRZn8XS9OnTeeWVV0wJmbOCIi+++CKDBw82JbjOyvgOGzaM\nO++80xTfvn17y/g+ffpQv359U/ydd95pGX/HHXdw7do1U7xVcgUQEBBAnz59TPFNmjSxjK9Tpw7t\n2rUzxTt7D5YtW5ZKlSqZ4p11JhkZGZw7d850/uLFi5bxR48eJTk52XT+/vvvt4zfsmUL//3vf03n\nAwICLBOsNWvW8Nprr5nOV6hQwTLBWr58uWX8rFmzLBOshQsXMmfOHMv4f/3rX6bzs2fPvqH4CRMm\nOMQrpfD29mbGjBm88MILpvgpU6bYpxznTXDHjRtnOeX47bffZunSpab44cOHW045Xrp0Kd+WkDLE\n06dPB4z/pgEBAfzrX/+yHLUWwsfHh8DAQAIDAxk9ejRaa44cOeJQHn7btm0kJCSQkJBg/5n19/d3\nSLj8/f1l5N+DeHt788qrrzJ2/Hiio6P5adUqLhw6RCU/Px4aOZLP+/W76WJlderUYeTIkYy0fSY8\nceIEsbGx1HRSkfrdd99lzpw59vdVREQErVu3lqTrJv1pgqW1/h14Mt/poX/x9coAU4EWQDawG+ij\ntd4LoJR6CvgMqIZtH6y/+DoerV+/fox94QUOnj5Nk5o1qVLXmxW8b4qrUtf4QHrw9GkS9u7l8379\nXN3UUqFMmTI3tDarVq1aTkcLrbRq1crpaKGV3I61oB544AEeeOCBAscPHjyYwYMHFzh+zJgxjBkz\npsDxU6ZMYcqUKQWOf/XVV3nppZdMCVnlypUt45977jnLEVBnCeITTzxhOQIaFBRkGX/fffdZjoA6\nm1IbGhrKyJEjTfFWyRUYH6B69eplircavQKoUaMGzZs3N8U7W/js5eWFj48POTk55OTkoLUmKyvL\nMhbg/PnzHD9+3PK8lf379xMbG2s636tXL8v4pKQkFi9e7PT1i4JSqirwIdATOANM0FovdRI7E/gH\nxhYjH2qtzcOyNmPHjiU2NpYNGzawfft2pyWVz5w5Q9WqVfHx+SvLoEVJpJSiUaNGNGrUiIEDBwLG\nz1hCQoI96UpMTGTPnj3s2bPHvibn1ltvdZhWePvttzudjSGKjwoVKjBs2DCXFCarU6eO5cOtXBs2\nbODQoUMcOnTI/ru4SpUqzJ0794Y+CwhHN1xFsMA3VioHaJy/imAh3bvEV2t6ZfJk1n31lX0xpDOZ\n165x94wZdO3bl1defdWFLRRCeLrcdaw5OTkopSw/8KelpXHhwgVTAlejRg3LUcqDBw9y7NgxU0Lc\nokULyyQ3OTmZHTt2MHjwYJdVEVRK5SZTw4DbgZVAqNZ6V764kRj7QObOMV8NvKm1Nj3xytsvXbly\nheTkZJo3b2751Lhv376sXr2aTp062Z8ad+7cGT+//MuQhfjDtWvX2LRpk0O1wlOnTjnElC1blpCQ\nEPuDuLCwsBua0SJKn+zsbLZv3+4wenrkyBG+/fZby8Jx27Zto1atWk5HxEoKl5dp/9MbKjUAY8e0\nkcAyIEZr/U4hv0aJT7Cys7MZ8PjjnN67t0CLIZfYqgAKIYQnclWZdqVUBeAc0Fprvd927lPgmNZ6\nQr7YWIw9Hz+wHQ8DntRam4Yrb6RfCgsLIz4+3uGcl5cXiYmJTtemCpGf1poDBw44rOPatWuXKa51\n69YO07+aNGki0wrFdR09epQaNWpYFlnp2LEjSUlJNG/e3J7IR0RE0KJFixL1vip2CZYrlIYEC4wk\na0pUFPPnzy+yxZBCCFEcuDDB6oCx56JvnnMvYOy52Cdf7HmgZ271W6VUEPCz1to0P/VG+6VTp045\njERs376ds2fPWn6gWbJkCW3atKFNmzaF8rv+0qVLREdH8/OqVaSnpeFXuTLde/WiXyGs/xDu9fvv\nvxMfH29PujZs2EBmZqZDTO3atR3Kw3fo0EG2KBEFkpOTw7333suvv/7KpUuXHK7t27ePpk2buqll\nhU8SrFIgb2d4IT2dSn5+0hkKIUoUFyZYEcDnWuu6ec49CQzQWnfPF5uFMdK1x3bcDPhNa23Kcm62\nX8rMzLRcO5Oamkr16tUB8PPzIzQ01P7huGvXrjf0xNj+0G7ePEL9/Xk4OPiPh3ZJScTv2cOo0aOZ\nFBkpD+1KiMzMTJKTkx325Pr9998dYipUqECnTp3s76vOnTs7XeMqBBjTVbds2WJ/X+3fv5/k5GTT\n76OcnBwmT55Mp06dCAsLs/8u8wSSYAkhhPB4Lh7BitFaV8xz7nmgi5MRrB5a6yTb8e3AGmcjWJF5\n9q3JX4b+rzpy5AgTJ04kJiaGQ7a9DsHYpHb//v0Fvk9Bp50Pe/99avn7y7TzEkprzZ49exymFe7d\nu9chRilF27ZtHaYVNrTYm1OIP7N9+3batm1rP27VqhXh4eF0797dctsUd8q/N2NUVJQkWEIIITyb\ni9dgpQIBedZgfQKkOFmD9aHWeqHtuFDWYP1VKSkp9g/F1atXJ9JiI9ItW7bw2muv2T8ct2rVCi8v\nrxsqnHTPzJl06dNHCieVEqdPn7ZPV42NjSU5OdlUAbN+/foO0wrbtWsnCbj4U4cPH2bBggXExsay\nfv16+/6hERER/Prrr25u3fXJCJYQQgiP56oEy/Zan2GUXR8OBALfAGFOqgg+g1HOHWAVRhXB/2dx\nz2LRL73xxhs8//zz9uOqVavSqVMnYn75hW2zZtG4Zk0iF3zN+ePZpq+tUtebqJEPcvD0aTpOmsSR\nY8dkGnopdPnyZTZs2GBP5uPi4kzbMlSqVInOnTvbk65OnTpRsWJFJ3cUAq5evcrGjRuJjY2lVq1a\nDBo0yBSzcuVKZs+ebU/kQ0ND3bZpu8cmWEqp5sBW4D9a6yds5wYA04Hq2PbB0lqbNlspLh2ZEEKI\nwuHiBCvvPlhngXFa62W29Vnfaq398sTOwEjENPD/tNYvOblnseiX9u3bx3fffWf/cJySkgKAf716\n/PbGGwA8G7mCv+0aAUA22XhjjESsaPU+b0b9DYD758zhoZEjXbJPjyjecnJy2Llzp0NRloMHDzrE\neHt706FDB3tVufDwcOrVq+emFgtP9eKLL/Laa6/Zj5VStGnThrFjx1omZEXJkxOsH4BywGGt9RNK\nqQAgHugNbAL+H+CltTZN0iwuHZkQQojC4coEqygUx35Ja82RI0cY0K8f9zVuzISHHgIcE6w3eINN\nbCKAAK7VSeXtcQPwr1OHj9eu5afUVBZHR7vzWxDF1PHjxx2mFW7atInsbMdR0caNG9tHIsLDwwkI\nCMDLy8tNLRae4OzZs8TFxdnXCCYlJXH16lUWLlxo+bDn7NmzVKlSpUg2bffIBEsp1Q/oC+wEmtkS\nrGlAI631IFvMbcAuoJrWOiPf1xe7jkwIIcRfJwlW0Xmwd2/+0bYtfTp2BBwTrKd5mt3sdoivUakS\nY+67j8S0NL769luXt1d4nosXL7J+/Xr7B+P4+HguXLjgEFOlShVCQ0PtSVfHjh1lCqq4ritXrpCU\nlIS/v7/lxsZ9+vTh559/tk9Xzd20vVKlSjf92jfbJxV+yvcnlFJ+QBTQHXgyz6UAIDb3QGt9QCl1\nFfDHGNESQgghxA3yq1yZcxkZltfmMY997GMb2/ih0v/I8L7AyfPnKVumDJX8/EzxCQkJtGjRgqpV\nqxZ1s4UHqVixIt27d6d7d2Ong+zsbLZt2+ZQHv7o0aN89913fPfddwD4+PgQFBTkMK2wVq1a7vw2\nRDFTrlw5IiIinF4/deoUFy9eZPXq1axevRowNm1fv349QUFBrmqmJZcnWMCrGPPYU/LVy68IpOWL\nTQNuPg0VQgghSqnuvXqxfMEChliUjffBh5a2f3zqn2PuK305dOYMoz/9lIdGjnSIzcrKokePHmRk\nZBAQEGD/UBwREUGTJk1uaE8uUbLlrsnq0KED//znPwFjy4G867i2bt1KYmIiiYmJvP766wA0a9bM\noTx8ixYt5H0lnEpISODkyZMO76sdO3bQunVry/jFixfTrl07AgICirwKpkunCNr2H1kMdNBaZyml\nIoGmtimCX2LsTTInT3w6xt4km/Ldp9hOxRBCCHHjZIpg0bl06RIN69Vjw9SpNLmJKoInT57kscce\nY/369WRmZtrP+/r6cv78+SJZByFKrvT0dBISEuwjXAkJCVy6dMkhpnr16oSFhdmTruDgYMsNuYXI\n5WzT9t9//50aNWoAf2zanvu+stq03aPWYCmlngWmAhcAhTFq5YWx1up7oHG+NVg7gepWa7CKYkNH\nIYQQrlHYmzq6W3FOsIAb2gfr7hkz6Nq3r9N9sDIzM+3llmNiYihXrhzRFsUwTpw4wTvvvENERASd\nO3emcmXT/sxC2F27do0tW7Y4TCs8ceKEQ0zZsmUJDg62j3CFhYVRvXp1N7VYeJLDhw8zYcIEYmNj\nOXz4sP1806ZN2bdvnyne0xKsckDeSd3/AhoBTwG1gTjgPmAz8B5GFcGBFvcp1h2ZEEKIGyMjWEUr\nOzubAY8/zum9e/lwxAiaWCwYP3j6NEMXLKB2ixYsiY6+6Sk00dHR9O9vFAJWStG2bVsiIiJ44IEH\nuOeee27q3qLk01pz6NAhe7KVO/0rv5YtWzpMK2zatKlMKxTXdezYMfu0QmebtntUgmV68TxTBG3H\n/YCZQDVkHywhhCg1JMEqetnZ2UyJimL+/Pl0bt6ch4OCqOrry7mMDJYnJ5Owdy+jR4/m5cmTC2V9\nwubNm1m8eDGxsbEkJydz7do1AJ5++mneeeedm76/KH1SU1OJj4+3fzhev349V65ccYipWbOmQ3n4\nwMBAbrnlFje1WHgqj06w/ipP6MiEEEIUnCRYrnPp0iWio6P5edUqLqSnU8nPj+69etGvX78iK5t9\n+YWAS5sAACAASURBVPJlNmzYQGxsLKGhoZbT+l9//XW+//57+0hEp06dqFixYpG0R5QMV69eZePG\njfZRrtjYWM6cOeMQU758eUJCQuzvq9DQUKpUqeKmFgtPIQmWEEIIjycJlrjnnnv44Ycf7Mfe3t60\nb9+eOXPm0K1bNze2THgKrTV79+51qCr322+/OcQopWjTpo1DFcxGjRrJtELhQBIsIYQQHs8VCZZS\nqirwIdATOANM0FovdRIbCUwErmAUZdJAO631ISfx0i/dpBMnTjgUONi0aRPZ2dkkJiYSEhJiij9+\n/Di1a9fGy8vLDa0VnuLMmTPExcXZ31tJSUn26aq56tat67COq127dlIVs5STBEsIIYTHc1GClZtM\nDQNuB1YCoVrrXRaxDmuEC3Bv6ZcKWUZGBomJidxxxx2Usah82LRpU1JTUwkLC7OPRoSEhFC+fHk3\ntFZ4iitXrpCUlGRP5OPi4khNTXWI8fX1pXPnzvakq3PnzlSqJNuyliaSYAkhhPB4RZ1gKaUqAOeA\n1lrr/bZznwLHtNYTLOIlwSrGLly4QJs2bThy5IjD+VtuuYWzZ8/Kh2FRYDk5Oezevdth9HT//v0O\nMV5eXrRv394+whUeHk79+vXd1GLhCpJgCSGE8HguSLA6ALFaa988514A7tRa97GIjwSeA7KBE8Db\nWuv3rnN/6Zfc4OjRow4fjLOzs9m6dasp7vLlyyxZsoSIiAhatGgh623EdZ08edJhHdemTZvIyspy\niGnYsKHDtMKAgIBCqb4pigdJsIQQQng8FyRYEcDnWuu6ec49CQzQWne3iG8JnAdOAZ2B5cAYrfUy\nJ/eXfqkYuHbtmuV0wl9++YUuXboAUL16dcLCwoiIiKBbt2507NjR1c0UHubSpUusX7/eYVphenq6\nQ4yfnx+hoaH2pCskJARfX18ndxTFnSRYQgghPN5Nd2ZKrQG6YBSjyC8WeAbzCNbzQBerESyL+48D\ngrXWjzq5rvNuVtm1a1fLUuTCPTZs2MCsWbOIjY3lxIkT9vN9+vThyy+/dGPLhCfKzs5mx44dDqOn\nhw8fdojx8fEhMDDQYVph7dq13dRi8WfWrl3L2rVr7cdRUVGelWAppRYBdwG+GNMuZmutF9qu3QXM\nBxoAicBQrfURi3tIgiWEECWIi9ZgpQIBedZgfQKkWK3Bsvj6sUCI1voRJ9elX/IAWmsOHTpk/1Ac\nFhbGE0+Yl9mtWLGCb7/91l48o1mzZjKtUFzXsWPHHKYVbtmyhZycHIeYpk2bOpSHb9mypVTBLKY8\nbgRLKdUK2Ke1vqaU8gfWAfcCR4D9GNWdvgGmAndorUMt7iEdmRBClCAuqiL4GcYI13AgEKOvCXNS\nRfBB4Bet9XmlVAjwBTBea73Yyb2lXypBhg4dyscff2w/rlmzJuHh4Tz33HPceeed7muY8BgXLlwg\nMTHRnszHx8eTkZHhEFO1alX7dNXw8HA6duxIuXLl3NRikZfHJVgOL65UC2ANxtSNqsBgrXXE/2/v\n3sOjqu99j7+/EMI9gtw8RhDkpiBiIJBCKKJ1S0/PPgL1PI+4a9V6Qa1ybPe29jxVgaDbavu4aytq\nvVS7vVS2FXFXd9tNHzXVJEAAy1XFcBdRAkgAEwSSfM8fM0wzySQmZjJrZvJ5+fCYWfObyWctFus3\n31m/9Vvh57oB+4Hz3f3Deq9TRyYikkYCuA/WfuDHJ6+pCl+j9Ud3zwo//h1wCZAJ7CY0ycUjTby3\n+qU0snbtWt54443Ih+N9+/YB8OqrrzJjRsMRpdXV1bpvkjSpurqa9evXR/apoqIi9uzZE9UmMzOT\n8ePHRwquyZMn069fv4ASt28pWWCZ2SPANUBX4F1gKnAf0Mndb6nTbgMwz92X1nu9OjIRkTSSiAKr\nLalfSl/uzpYtWygqKmLGjBmceuqpDdpMnTqVioqKqOttzjzzTA0rlEa5Ozt37oy6jmvjxo3UP46M\nHDkyaljh8OHDtV8lQEoWWAAW2jsmAdOAnwG/BsrrjoU3syLgCXd/tt5r1ZGJiKQRFViSqqqrq+nT\np0+DWeWys7MpLS3l9NNPb+SVItEqKipYvnx5pOgqLS3l6NGjUW369esXVXCNGzeOzMzMgBKnr5Qt\nsCIBzB4D3gOGAhnufmud59YD83UGS0QkvanAklT2xRdfsHr16sgkB8XFxbg7+/fvbzCJgbvz5ptv\nMnHiRN0QWZp0/Phx1q5dGzWssLy8PKpNly5dmDBhQtSwwt69eweUOH2kQ4H1JPA5sAm4ps41WN2B\nciAn1jVYmg5XRCR1xXtK3KCpwJK6amtr2bNnD2eccUaD57Zs2cLw4cPp0KEDY8eOjRpWGKu9yEnu\nztatW6OGFb7/foM5ehg9enTUfjVkyBANK2yhlCqwzKwfcBGhmZuOErrQ+GXgCmAFUEZoFsE/AgsJ\nzSI4Ocb7qCMTEUkjOoMl7cWqVau45ZZb+Nvf/kZ1dXVk+cSJE1m5cmWAySQVHThwgJKSkkjRtWrV\nKo4fPx7V5rTTTosUW1OmTGHs2LExb8gtf5dqBVZfQgXVeUAHYCfwS3d/Ovz8RcAjwCBC98G6RvfB\nEhFJfyqwpL2prKyktLQ0MqRw4sSJFBQUNGhXWlrKsmXLyM/PZ+LEiXTv3j3Gu4mEfPHFF6xZsyZq\nuOqBAwei2nTr1o28vLxI0TVp0iSysrICSpycUqrAihd1ZCIi6UUFlkhsd955J/fddx8AGRkZ5OTk\nkJ+fz+zZs8nLyws4nSQ7d2fz5s2RIYXFxcWUlZVFtenQoQNjxoyJGlY4aNCggBInBxVYIiKS8lRg\nicT21ltvsXTpUoqLi1m7di21tbUAPProo9x8880Bp5NUtHfv3qhhhWvWrIkargowcODAqNkKx4wZ\nQ8eOHQNKnHgqsEREJOWpwBL5ckeOHGHlypUUFRVx5ZVXMmzYsAZtbrzxRj7++OPImYgJEybQpUuX\nANJKqqiqqmLVqlVRwwoPHToU1aZnz55MmjQpUnTl5eXRo0ePgBK3PRVYIiKS8lRgibSeu5Odnc0n\nn3wSWZaZmUlubi7PPvssQ4cODTCdpIra2lree++9qGGF27dvj2rTsWNHzj///Eghn5+fn1b3fFOB\nJSIiKS8RBZaZ3QJcA4wBfufu135J+x8CdwBdgCXAze5+opG26pckcO7Orl27oj4Yb9iwgQ4dOnDw\n4MGY993auXMngwYN0jTe0qQ9e/ZETQ+/du1aampqotoMGTIk6jquUaNGNbgPXKpQgSUiIikvQQXW\nTKAWmA50barAMrPpwG+BC4FPgFeB5e7+k0baq1+SpFRRUcHGjRuZMmVKg+cOHz5M79696dOnT9T1\nNuPGjSMzMzOAtJIqPv/8c1auXBkpulasWMGRI0ei2vTq1YvJkydH9qsJEybQtWvXgBK3jAosERFJ\neYkcImhm9wDZX1JgvQBsd/e7wo8vAl5w9//RSHv1S5Jy1q1bx/Tp09m7d2/U8mHDhjWYaU6kKTU1\nNWzYsCFyhquoqIjdu3dHtenUqRPjxo2LGlbYv3//gBI3TQWWiIikvCQssNYC/+ruvw8/7gOUA33d\n/WCM9uqXJCW5O9u2bYv6YDx27FhefPHFBm137NjBO++8Q35+PkOGDNGwQmnSrl27ooYVrl+/nvrH\nyeHDh0cNKxw5cmRS7FcpVWCZWSbwKHAx0BvYAtzp7n8OP/8NYBEwkNCNhr+nGw2LiKS/JCywtgDf\nd/dl4ccZwHFgsPolSXfV1dVkZGQ0WP7II49w6623AnDaaadFPhRPnz6dc845J9ExJcUcOnSIFStW\nRIqulStXUlVVFdWmb9++UcMKx48fT+fOnROeNdUKrG7A7cAz7v6Rmf0v4EXgXKAS2ApcC7wO3At8\n3d0nxXgfdWQiImmk1Z2Z2VvABUCszqHY3afWadvcM1j3uvvL4cenAvto4gzW/PnzI4+nTZvGtGnT\nvuLaiCSn119/nSeffJLi4mIOHDgQWX733XezcOHCAJNJKjpx4gTr1q2LOnv66aefRrXp3Lkzubm5\nkWJ+8uTJ9OnTJ+5ZCgsLKSwsjDwuKChInQIrZgCzdcACoC9wtbtPCS/vBuwHznf3D+u9RgWWiKSN\n+XPmU/FhRYPlvUb0ouCJggASJV4SnsF6Adjm7neHH18EPO/uMechVr8k7Ym7s3nz5siH4uuuuy7m\nJBoPPvgg27Zti3w4HjRoUABpJVW4O9u3b4+aBXPTpk0N2p1zzjlR13ENHTo07sMKU+oMVoNfbjYA\n2A6cD3wf6OTut9R5fgMwz92X1nudOjIRSRu3TbuNWX+d1WD50guW8svCXwaQKPESNItgR6ATMA84\nA7gBqHb3mhhtpwPPAN8APgVeBla4+52NvLf6JZF6cnNzWbNmTeTxwIEDyc/PZ8GCBYwcOTLAZJIq\nPvvsM5YvXx4pukpLSzl27FhUmwEDBkTNgpmTk0OnTp1a9XtTtsAKj2f/E1Dm7t83s6eA8rpT4JpZ\nEfCEuz9b77XqyEQkbajASliBNR+YT/QwwgJ3X2hmA4FNwCh33x1u/wPg/xG6D9bL6D5YIi1SVFTE\nO++8Q1FRESUlJVRUhM7Ub926lbPOOqtB++PHj2t6eGnSsWPHePfddyNnuIqKiti/f39Um65du5KX\nlxcpuiZNmkSvXr1a9Hta2yc1vIIxASx0Hu954BgwN7z4cyCrXtMs4AgxLFiwIPKzxrqLiKSW+uPd\nE8HdC4CYYy7d/SPq9UHu/hDwUAKiiaSlKVOmRIYO1tbW8t5771FaWsqQIUMatK2trSU7O5vBgwdH\nPhjn5+dz+ukxR+VKO9W5c2cmTZrEpEmTuP3223F3ysrKooYVbt68OaqPMTPOPffcqGGFZ555ZpvO\nVhjIGSwzexoYBHzL3Y+Hl91A9DVY3QlNiZuja7BEJJ3pDFZir8FqC+qXRFpny5YtnH322dTURI/Y\nHTVqFBs2bKBDhw4BJZNUs2/fPkpKSiJF1+rVqzlxInrwQXZ2dtT08Oedd17UzJkpdwbLzH4NnA1c\nfLK4ClsK/MzMZgF/JDRGfl394kpERERE0suwYcOoqKigtLQ08sG4pKSEvn37xiyuKioqWL9+PRMm\nTKBr164BJJZk1a9fP2bMmMGMGTMAOHr0KKtXr44MKSwpKeHjjz/mpZde4qWXXgKgR48efO1rX4sU\nXa2V6GnaBwE7gC+Ak19ROHCju78YnqXpEUJnt1YC1+h+IyKS7jSLoM5giUhD1dXVHDhwgAEDBjR4\n7pVXXuGyyy6jU6dOjBs3Lmr4V//+/QNIK6mitraWDz74IGp6+G3btjVol5KTXLSGOjIRkfSiAktE\nWuKVV15h4cKFrF+/nrr/9q677jqeeuqpAJNJKvrkk0+ihhWuWrVKBZaIiKQ2FVgi8lUcOnSIFStW\nRCY4uP7667niiisatFu6dCllZWXk5+eTm5tL586dA0grqSJlp2lvDXVkIiLpRQWWiLSlSy+9lNde\new0IzUSXm5tLfn4+119/PcOHDw84nSQbFVgiIpLyVGCJSFtasmQJf/nLXygqKmLTpk2R5e+8805c\nJjWQ9KICS0REUp4KLBFJlIMHD7J8+XKKioqYN28eXbp0adBm2rRp9O3bNzJxRk5ODp06dYrL76+q\nqmLx4sW8uWwZhw8dIuuUU7jokkuYPXs23bp1i8vvkNZRgSUiIilPBZaIJIv9+/fTr1+/qGVdu3Yl\nLy+PP/3pTzELsuaoqanhnoICFj38MJNGjOCy3Fx6d+/OwcpKlqxezfIPP+TWuXO5e/58OnbsGI9V\nka9IBZaIiKS8RBRYZnYLcA0wBvidu1/bRNurgd8AVYARuqXIP7r72420V78kkibcnbKyssgU3sXF\nxWzevJlhw4ZRVlbWoP2JEyfYs2cPgwYNwiz2YaympoZ/uvxyysvKeHrOHIbEmEp+e3k51z7xBANG\njOCFxYtVZAVIBZaIiKS8BBVYM4FaYDrQtRkF1nXuPrWZ761+SSSN7du3j48++ohx48Y1eG7FihVM\nmjSJ7OzsyI1q8/PzOe+888jIyABgwbx5/PU//5M///jHdG5iqOGxEyf45gMPcMGMGSxYuLDN1kea\nlnIFVlPfIJrZN4BFwEBCNxr+nm40LCKS/hI5RNDM7gGyVWCJSDwsWbKEG264gYMHD0YtnzlzJkuX\nLqWqqopB2dmsvvdeBvfvz/zH/0DFnpoG79Pr9I4U3Hgp28vLmXD33ezavVvXZAWktX1SRjzDNNPH\nwD2Ev0E8udDM+gBLgGuB14F7gf8AJgWQUUREJMfMyoHPgOeB+9y9NuBMIpJkLrvsMmbNmsUHH3wQ\nGVJYXFxMbm4uAIsXL2bSiBEMDg8L/GBrOSN35DGGMfTj79d6LeUJAIb078/Xhg9n8eLFXHtto98D\nSRJLeIHl7q8CmNkEILvOU98GNrr7K+HnFwD7zWyEu3+Y6JwiItKu/RU41913mtlo4CXgBPBAsLFE\nJBl16NCBUaNGMWrUKObMmQNAbW3o+5g3ly3jsnCxBfBexVZeYhkAAxjAueH/jpyojLS5bPx43li2\nTAVWigriDFZjRgPrTj5w9yoz2xpergJLREQaZWZvARcQmoyivuLmDvU7yd131Pl5k5ktBG6niQJr\nwYIFkZ+nTZvGtGnTWvIrRSTNdOjQAYDDhw7Re9CgyPL+XfowkT5sYhN7w/+9wRv8z6P5kTa9u3fn\nyI4diY7cbhUWFlJYWBi390umAqsHUF5v2SGgZwBZREQkhbj7hQn4NU2Ox69bYImInJR1yikcrPz7\n2alzew9l1qdzqKGGHexgAxvYyEZO7/r3j7wHKyvpmZUFwNy5c+nUqVNk8owBAwYkfB3SXf0vxQoK\nClr1fslUYH0OZNVblgUcidVY3xSKiKSueH9b2Bxm1hHoBHQEMsysM1Dt7g2uNjezbwLvunu5mZ0N\n3EXoumARkRa56JJLWPL441xT77NqRzoyNPzfTGaytNMTkeeWrFnDt2+8kRMnTvD0009TVVXFL37x\nCwCGDh3KlClTeOihh+jVq1ciV0WaKbBp2uvP4mRmNwBXu/uU8OPuhM5o5dS/BkuzNYmIpJcETdM+\nH5hP9DDCAndfaGYDgU3AKHffbWY/B74LdAf2As8B98YqxsLvrX5JRGI6OYvgqnvvZUgLZxHMzMyk\nsLAwMnnG8uXLqayspGfPnhw8eDDmvbKOHTtG586dE7FqaSsVp2k/+Q3iPOAM4AagGugNlBGaRfCP\nwELg6+4+OcZ7qCMTEUkjiZymvS2oXxKRprTkPljT77+faTNnxrwPVnV1NevXr2fnzp3MmjWrwfO7\ndu1i+PDh5ObmRu7JNXnyZPr27RvX9Ul3qVhgNfUN4kXAI8AgQvfBukb3wRIRSX8qsEQkndXU1PBP\nl19OeVkZT8+Zw5DwlO11bS8v53uPP85pI0fywuLFMc9OfZnXXnuNGTNmUP94NGvWLF555ZWvnL+9\nSbkCKx7UkYmIpBcVWCKS7mpqarinoIBFixbxteHDuWz8eHp3787BykqWrFnDirIy5s6dy13z5n2l\n4uqkiooKli9fHhlWWFpayrXXXsuiRYsatN2xYweffvop48aNIzMzszWrl1ZUYImISMpTgSUi7UVV\nVRWLFy/mzWXLOHL4MD2zsrjokkuYPXs23bp1i/vvO378OJWVlfTu3bvBcwUFBSxYsIAuXbowceJE\n8vPzI3/a8wQaKrBERCTlqcASEUm8hx56iCeeeIL333+/wfLbbrstoFTBU4ElIiIpTwWWiEhwDhw4\nQElJCcXFxRQVFfGrX/2KcePGNWi3aNEiampqmDJlCmPHjiUjI5nu+BQ/KrBERCTlqcASEUl+Q4YM\nYceOHQB0796dvLw8pkyZwty5c9NqpkIVWCIikvJUYImIJLfa2lqeeeaZyOQZZWVlQOj4feDAgZjX\neKUqFVgiIpLyVGCJiKSWvXv3UlJSQllZGXfccUeD56uqqhgzZgx5eXmRe3Kde+65rZohMVHSrsAy\ns97A08A/APuAn7j7i/XaqCMTEUkjbV1gmVkm8ChwMaEb228B7nT3Pzfxmh8CdwBdgCXAze5+opG2\n6pdEROooLCzkwgsvjFqWlZXFpZdeynPPPRdQquZpbZ/UIZ5h4uRR4AugH3Al8JiZnRNsJBERSXEZ\nwC7g6+5+CjAPeMnMBsVqbGbTCRVXFwKDgaFAQWKiioikvqlTp7J+/Xoee+wxvvOd7zB48GAOHz5M\nZWVlzPYVFRXs2bMnwSnbRlIVWGbWDfg2cJe7H3X3YuAPwHeDTZY8CgsLg44QCK13+9Ne1729rndb\nc/cqd1/o7h+FH/8XsB0Y38hLrgJ+4+4fuPsh4B7ge4lJmzipvL8pe+Klam5Q9iC8/fbbjBkzhptu\nuonnn3+e7du3s3v3bu6///6Y7X//+9+TnZ3NWWedxVVXXcXjjz/Oxo0bqa2tTXDy1kuqAgsYAVS7\n+9Y6y9YBowPKk3RS9R9Za2m925/2uu7tdb0TzcwGAMOBTY00GU2o/zlpHdA/PIw9baTy/qbsiZeq\nuUHZgxArd3Z2NiNGjIjZvqKigh49erB9+3aee+45brrpJsaMGcO8efPaOGn8JVuB1QM4VG/ZIaBn\nAFlERCQNmVkG8DzwW3f/sJFm9fujQ4Ch/khEpE386Ec/4uDBg7z77rs8/PDDzJ49mzPOOIO8vLyY\n7V9//XWWLl1KeXl5gpN+uWS7O9jnQFa9ZVnAkQCyiIhIijCzt4ALgFgzTRS7+9RwOyNUXB0D5jbx\nlvX7o6zwe6s/EhFpIxkZGeTk5JCTk8Ott94K0OgQwfvuu4/ly5cDMGLEiMhMhTNnzuTUU09NWOZY\nkmoWwfA1WJ8Bo08OEzSzfwc+dvef1GmXPKFFRCQuEjFNu5k9DQwCvuXux5to9wKwzd3vDj++CHje\n3U9vpL36JRGRNJJu07T/jtC3hDcAOcDrwGR3fz/QYCIiktLM7NfAecDF7l71JW2nA88A3wA+BV4G\nVrj7nW0eVEREUlqyXYMFcAvQDSgHXgBuUnElIiKtEZ6OfQ5wPrDXzI6Y2WEzuyL8/MDw4zMA3P2/\ngZ8BbxGabXA7sCCQ8CIiklKS7gyWiIiIiIhIqkrGM1giIiIiIiIpKaUKLDPrbWZLzexzM9t+cmhH\nujOzW8xslZl9Eb5Au10ws0wze8rMdpjZITNbY2bfDDpXIpjZc2a2J7zeH5jZdUFnSiQzG25mR83s\n2aCzJIqZFYbX+XB4+Fq7GRptZrPN7L3wsb3MzPKDztSYr3JcMrMfmtknZnYw/NpOicobI0uz+xMz\nu9rMquvsk4fNbGqissbI06K+MMm2e7M/v5jZfDM7Xm+7D07SrA+Y2X4z22dmDyQqYyNZmpU76O3b\nSKaW/LtMpv26WbmT7VgSztSiY3lLt3tKFVjAo8AXQD/gSuAxMzsn2EgJ8TFwD/CboIMkWAawC/i6\nu58CzANestC1FOnuPuDM8HpfCtxrZjkBZ0qkRUBp0CESzIHvu3uWu/d09/ZwbMPM/gH4KXC1u/cA\npgLbgk3VpBYdlyw0WcYdwIXAYGAoUJCYqDG1tD8pqbNPZrn7222Y7cs0O3sSbveWfn5ZXG+770hE\nyLBmZTWzGwn1T2MITR7zj2Y2J4E562vJNg5y+8bSrH07CffrlhxPkulYAi04ln+V7Z4yBZaFpnD/\nNnCXux9192LgD8B3g03W9tz9VXf/A6Ep7NsNd69y94Xu/lH48X8RutB8fLDJ2p67v+/uJ8IPjdCH\n76EBRkoYM5sNHATeCDpLANp8mvIktABY6O6rANz9E3f/JNhIjfsKx6WrgN+4+wfufojQh5HvJSZt\nQ6ncn7Qwe9Js91T6/NLCrFcBD9b5N/sgcE3CwtaRSts4lhbs20mzX0PKH09acixv8XZPmQILGAFU\nn7w/Vtg6YHRAeSTBzGwAMBzYFHSWRDCzR8ysEngf2AP8MeBIbc7Msgh9K/QvtM9i46dmVm5m75jZ\nBUGHaWtm1gHIBfqHhwbuMrOHzaxz0NmaqxnHpdGE+qqT1hFa395tnS1OcsL75Admdlf47ywVJNN2\n/yqfX/53eOjdBjO7qW3jRWlJ1ljbOKjPZC3dxkFt39ZKpv26pZL6WPIlx/IWb/ekWrkv0QM4VG/Z\nIaBnAFkkwcwsA3ge+K27fxh0nkRw91sI7fdTgFeAY8EmSoiFwJPu/nHQQQJwB3AWkA08CbxmZkOC\njdTmBgCdgMuAfEJTqOcAdwUZqrmaeVyq33cdIvTlQSr0XX8FznX3/oT+jq4AfhRspGZLpu3e0s8v\n/wGcQ2io2xxgnpld3nbxorQka6xt3KONcn2ZluQOcvu2VjLt1y2R1MeSZhzLW7zdU6nA+hzIqrcs\nCzgSQBZJIDMzQjv+MWBuwHESykNKgIHAzUHnaUtmdj5wMfBQ0FmC4O6r3L3S3U+4+7NAMfCtoHO1\nsaPh///K3cvd/TPg3whwvc3sLTOrNbOaGH/ertOuucel+n1XFqEhv3Hvu5qbvbncfYe77wz/vInQ\nFyD/J965If7ZSa7t/jlwSr2XNfr5JTwM6dPw8X858EvaaLvH0JLPWrG28edtlOvLNDt3wNu3tRK2\nX8dTIo8lLdXMY3mLt3tGvAImwIdAhpkNrXMKeCztZLhYO/cboC/wLXevCTpMQDJI/2uwLgDOBHaF\nD3g9gI5mNsrdc4ONFggnzYdJunuFme0OOkdd7n5hM5s297i0iVBf9XL48fnAXnc/+NVTxtaC7K3R\nJvtkG2RPmu0evj6oYys+vyTyWNCSz1ont/Hq8OPzG2mXCK35jJhKx9qE7dcJkCzbvDnH8hZv95Q5\ng+XuVYSGSS00s24Wmsb3UuC5YJO1PTPraGZdgI6EDiCdzaxj0LkSwcx+DZwNXOrux4POkwhm1s/M\nLjez7mbWITx7zWzSf9KHxwkVkecTOpD9GngduCTIUIlgZqeY2SUn/22b2XeArwP/HXS2BHgGFcc8\ntAAABABJREFUmBve73sDPwBeCzhTk1p4XHoWuM7Mzgmv352E1jkQLelPzOybZtY//PPZhIZuvpq4\ntA3ytKQvTJrt3tLPL2Z2qZn1Cv88Efi/JGi7tzDrs8A/m9npZnY68M+kwDYOcvs2pgX7dtLs19D8\n3Ml2LDmpBcfylm93d0+ZP0BvYCmhU3U7gMuDzpSg9Z4P1AI1df7MCzpXAtZ7UHi9qwidhj0CHAau\nCDpbG693X6CQ0Kw8FYQuprw26FwBbIf5wLNB50jg33kpoXHdnwElwEVB50rQumcAjxCaOXIP8Asg\nM+hcTeRt8rhEaDjvYeCMOq/5AfBp+N/zU0CnAPM32p/Uzw78PJz7CLAl/NqOqZA9Cbd7o59fCF1n\ne7jO498B+8Pr8x5wSzJkrZ8zvOx+4EA470+D2r4tyR309m0ke8x9O7xfH0ni/bpZuZPtWBLO1Oix\nPB7HEwu/SERERERERFopZYYIioiIiIiIJDsVWCIiIiIiInGiAktERERERCROVGCJiIiIiIjEiQos\nERERERGROFGBJSIiIiIiEicqsEREREREROJEBZaIiIiIiEicqMASERERERGJExVYIiIiIiIicaIC\nS0REREREJE4ygg4g0t6Z2TjgSsCBM4EbgBuBXkA2MM/dtweXUERE2gv1SSKtpwJLJEBmNgy42t1v\nCz9+BlgBXE3oDPM7wLvALwILKSIi7YL6JJH4UIElEqwfAD+q87g78Jm7rzCzM4AHgX8PJJmIiLQ3\n6pNE4sDcPegMIu2WmQ1094/qPN4NPOPud8doOx64ClgN5AM/d/etCQsrIiJpTX2SSHzoDJZIgOp1\nZGcDpwNv1W9nZpnAEiDP3fea2fvAi8DERGUVEZH0pj5JJD40i6BI8rgYOAaUnFxgZkPCP04Fjrj7\nXgB3Xw2cY2aDE5xRRETaB/VJIl+RCiyRgJhZFzN7wMxGhxddDKx39y/CzxvwL+HnBgMH6r3FQWA0\nIiIiraQ+SSR+NERQJDjfAm4H1phZNXAWUFHn+TuB58I/9wOq6r3+C6BnW4cUEZF2QX2SSJyowBIJ\nzl+BZ4DxQC6QBzxqZo8Bx4E/uPvKcNsKwOq9vgewP0FZRUQkvalPEokTzSIokgLM7ELg39w9J/y4\nI1AJjHH3skDDiYhIu6I+SaRpugZLJDW8DfQzs4Hhx9OATerIREQkAOqTRJqgIYIiKcDda8zsu8BP\nzGw5oc7s8mBTiYhIe6Q+SaRpGiIoIiIiIiISJxoiKCIiIiIiEicqsEREREREROJEBZaIiIiIiEic\nqMASERERERGJExVYIiIiIiIicaICS0REREREJE5UYImIiIiIiMSJCiwREREREZE4UYElIiIiIiIS\nJ/8f4Vw1VcOg4J4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f112dc600f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Xs = np.array([[1, 50], [5, 20], [3, 80], [5, 60]]).astype(np.float64)\n",
    "ys = np.array([0, 0, 1, 1])\n",
    "svm_clf = SVC(kernel=\"linear\", C=100)\n",
    "svm_clf.fit(Xs, ys)\n",
    "\n",
    "plt.figure(figsize=(12,3.2))\n",
    "plt.subplot(121)\n",
    "plt.plot(Xs[:, 0][ys==1], Xs[:, 1][ys==1], \"bo\")\n",
    "plt.plot(Xs[:, 0][ys==0], Xs[:, 1][ys==0], \"ms\")\n",
    "plot_svc_decision_boundary(svm_clf, 0, 6)\n",
    "plt.xlabel(\"$x_0$\", fontsize=20)\n",
    "plt.ylabel(\"$x_1$  \", fontsize=20, rotation=0)\n",
    "plt.title(\"Unscaled\", fontsize=16)\n",
    "plt.axis([0, 6, 0, 90])\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()\n",
    "X_scaled = scaler.fit_transform(Xs)\n",
    "svm_clf.fit(X_scaled, ys)\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.plot(X_scaled[:, 0][ys==1], X_scaled[:, 1][ys==1], \"bo\")\n",
    "plt.plot(X_scaled[:, 0][ys==0], X_scaled[:, 1][ys==0], \"ms\")\n",
    "plot_svc_decision_boundary(svm_clf, -2, 2)\n",
    "plt.xlabel(\"$x_0$\", fontsize=20)\n",
    "plt.title(\"Scaled\", fontsize=16)\n",
    "plt.axis([-2, 2, -2, 2])\n",
    "\n",
    "save_fig(\"sensitivity_to_feature_scales_plot\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Sensitivity to outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure sensitivity_to_outliers_plot\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA1gAAAC7CAYAAAB4pz8ZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAex0nAGD+3CIAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f112db95710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_outliers = np.array([[3.4, 1.3], [3.2, 0.8]])\n",
    "y_outliers = np.array([0, 0])\n",
    "Xo1 = np.concatenate([X, X_outliers[:1]], axis=0)\n",
    "yo1 = np.concatenate([y, y_outliers[:1]], axis=0)\n",
    "Xo2 = np.concatenate([X, X_outliers[1:]], axis=0)\n",
    "yo2 = np.concatenate([y, y_outliers[1:]], axis=0)\n",
    "\n",
    "svm_clf2 = SVC(kernel=\"linear\", C=10**9)#float(\"inf\"))\n",
    "svm_clf2.fit(Xo2, yo2)\n",
    "\n",
    "plt.figure(figsize=(12,2.7))\n",
    "\n",
    "plt.subplot(121)\n",
    "plt.plot(Xo1[:, 0][yo1==1], Xo1[:, 1][yo1==1], \"bs\")\n",
    "plt.plot(Xo1[:, 0][yo1==0], Xo1[:, 1][yo1==0], \"yo\")\n",
    "plt.text(0.3, 1.0, \"Impossible!\", fontsize=24, color=\"red\")\n",
    "plt.xlabel(\"Petal length\", fontsize=14)\n",
    "plt.ylabel(\"Petal width\", fontsize=14)\n",
    "plt.annotate(\"Outlier\",\n",
    "             xy=(X_outliers[0][0], X_outliers[0][1]),\n",
    "             xytext=(2.5, 1.7),\n",
    "             ha=\"center\",\n",
    "             arrowprops=dict(facecolor='black', shrink=0.1),\n",
    "             fontsize=16,\n",
    "            )\n",
    "plt.axis([0, 5.5, 0, 2])\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.plot(Xo2[:, 0][yo2==1], Xo2[:, 1][yo2==1], \"bs\")\n",
    "plt.plot(Xo2[:, 0][yo2==0], Xo2[:, 1][yo2==0], \"yo\")\n",
    "plot_svc_decision_boundary(svm_clf2, 0, 5.5)\n",
    "plt.xlabel(\"Petal length\", fontsize=14)\n",
    "plt.annotate(\"Outlier\",\n",
    "             xy=(X_outliers[1][0], X_outliers[1][1]),\n",
    "             xytext=(3.2, 0.08),\n",
    "             ha=\"center\",\n",
    "             arrowprops=dict(facecolor='black', shrink=0.1),\n",
    "             fontsize=16,\n",
    "            )\n",
    "plt.axis([0, 5.5, 0, 2])\n",
    "\n",
    "save_fig(\"sensitivity_to_outliers_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Large margin *vs* margin violations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is the first code example in chapter 5:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=(('scaler', StandardScaler(copy=True, with_mean=True, with_std=True)), ('linear_svc', LinearSVC(C=1, class_weight=None, dual=True, fit_intercept=True,\n",
       "     intercept_scaling=1, loss='hinge', max_iter=1000, multi_class='ovr',\n",
       "     penalty='l2', random_state=None, tol=0.0001, verbose=0))))"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn import datasets\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.svm import LinearSVC\n",
    "\n",
    "iris = datasets.load_iris()\n",
    "X = iris[\"data\"][:, (2, 3)]  # petal length, petal width\n",
    "y = (iris[\"target\"] == 2).astype(np.float64)  # Iris-Virginica\n",
    "\n",
    "svm_clf = Pipeline((\n",
    "        (\"scaler\", StandardScaler()),\n",
    "        (\"linear_svc\", LinearSVC(C=1, loss=\"hinge\")),\n",
    "    ))\n",
    "\n",
    "svm_clf.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm_clf.predict([[5.5, 1.7]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's generate the graph comparing different regularization settings:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=(('scaler', StandardScaler(copy=True, with_mean=True, with_std=True)), ('linear_svc', LinearSVC(C=100, class_weight=None, dual=True, fit_intercept=True,\n",
       "     intercept_scaling=1, loss='hinge', max_iter=1000, multi_class='ovr',\n",
       "     penalty='l2', random_state=None, tol=0.0001, verbose=0))))"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler = StandardScaler()\n",
    "svm_clf1 = LinearSVC(C=1, loss=\"hinge\")\n",
    "svm_clf2 = LinearSVC(C=100, loss=\"hinge\")\n",
    "\n",
    "scaled_svm_clf1 = Pipeline((\n",
    "        (\"scaler\", scaler),\n",
    "        (\"linear_svc\", svm_clf1),\n",
    "    ))\n",
    "scaled_svm_clf2 = Pipeline((\n",
    "        (\"scaler\", scaler),\n",
    "        (\"linear_svc\", svm_clf2),\n",
    "    ))\n",
    "\n",
    "scaled_svm_clf1.fit(X, y)\n",
    "scaled_svm_clf2.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# Convert to unscaled parameters\n",
    "b1 = svm_clf1.decision_function([-scaler.mean_ / scaler.scale_])\n",
    "b2 = svm_clf2.decision_function([-scaler.mean_ / scaler.scale_])\n",
    "w1 = svm_clf1.coef_[0] / scaler.scale_\n",
    "w2 = svm_clf2.coef_[0] / scaler.scale_\n",
    "svm_clf1.intercept_ = np.array([b1])\n",
    "svm_clf2.intercept_ = np.array([b2])\n",
    "svm_clf1.coef_ = np.array([w1])\n",
    "svm_clf2.coef_ = np.array([w2])\n",
    "\n",
    "# Find support vectors (LinearSVC does not do this automatically)\n",
    "t = y * 2 - 1\n",
    "support_vectors_idx1 = (t * (X.dot(w1) + b1) < 1).ravel()\n",
    "support_vectors_idx2 = (t * (X.dot(w2) + b2) < 1).ravel()\n",
    "svm_clf1.support_vectors_ = X[support_vectors_idx1]\n",
    "svm_clf2.support_vectors_ = X[support_vectors_idx2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure regularization_plot\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA1gAAADfCAYAAADrwnJVAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nIYST/D/VrQE81tnnrQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f112dba9940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,3.2))\n",
    "plt.subplot(121)\n",
    "plt.plot(X[:, 0][y==1], X[:, 1][y==1], \"g^\", label=\"Iris-Virginica\")\n",
    "plt.plot(X[:, 0][y==0], X[:, 1][y==0], \"bs\", label=\"Iris-Versicolor\")\n",
    "plot_svc_decision_boundary(svm_clf1, 4, 6)\n",
    "plt.xlabel(\"Petal length\", fontsize=14)\n",
    "plt.ylabel(\"Petal width\", fontsize=14)\n",
    "plt.legend(loc=\"upper left\", fontsize=14)\n",
    "plt.title(\"$C = {}$\".format(svm_clf1.C), fontsize=16)\n",
    "plt.axis([4, 6, 0.8, 2.8])\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.plot(X[:, 0][y==1], X[:, 1][y==1], \"g^\")\n",
    "plt.plot(X[:, 0][y==0], X[:, 1][y==0], \"bs\")\n",
    "plot_svc_decision_boundary(svm_clf2, 4, 6)\n",
    "plt.xlabel(\"Petal length\", fontsize=14)\n",
    "plt.title(\"$C = {}$\".format(svm_clf2.C), fontsize=16)\n",
    "plt.axis([4, 6, 0.8, 2.8])\n",
    "\n",
    "save_fig(\"regularization_plot\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Non-linear classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure higher_dimensions_plot\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAsMAAAEWCAYAAACDl5pDAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f112dbf7860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X1D = np.linspace(-4, 4, 9).reshape(-1, 1)\n",
    "X2D = np.c_[X1D, X1D**2]\n",
    "y = np.array([0, 0, 1, 1, 1, 1, 1, 0, 0])\n",
    "\n",
    "plt.figure(figsize=(11, 4))\n",
    "\n",
    "plt.subplot(121)\n",
    "plt.grid(True, which='both')\n",
    "plt.axhline(y=0, color='k')\n",
    "plt.plot(X1D[:, 0][y==0], np.zeros(4), \"bs\")\n",
    "plt.plot(X1D[:, 0][y==1], np.zeros(5), \"g^\")\n",
    "plt.gca().get_yaxis().set_ticks([])\n",
    "plt.xlabel(r\"$x_1$\", fontsize=20)\n",
    "plt.axis([-4.5, 4.5, -0.2, 0.2])\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.grid(True, which='both')\n",
    "plt.axhline(y=0, color='k')\n",
    "plt.axvline(x=0, color='k')\n",
    "plt.plot(X2D[:, 0][y==0], X2D[:, 1][y==0], \"bs\")\n",
    "plt.plot(X2D[:, 0][y==1], X2D[:, 1][y==1], \"g^\")\n",
    "plt.xlabel(r\"$x_1$\", fontsize=20)\n",
    "plt.ylabel(r\"$x_2$\", fontsize=20, rotation=0)\n",
    "plt.gca().get_yaxis().set_ticks([0, 4, 8, 12, 16])\n",
    "plt.plot([-4.5, 4.5], [6.5, 6.5], \"r--\", linewidth=3)\n",
    "plt.axis([-4.5, 4.5, -1, 17])\n",
    "\n",
    "plt.subplots_adjust(right=1)\n",
    "\n",
    "save_fig(\"higher_dimensions_plot\", tight_layout=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZYAAAEdCAYAAAAvj0GNAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nikVERIJSxSIiIkGpYhERkaD+PyxX+smTKkCdAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f112dc33f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.datasets import make_moons\n",
    "X, y = make_moons(n_samples=100, noise=0.15, random_state=42)\n",
    "\n",
    "def plot_dataset(X, y, axes):\n",
    "    plt.plot(X[:, 0][y==0], X[:, 1][y==0], \"bs\")\n",
    "    plt.plot(X[:, 0][y==1], X[:, 1][y==1], \"g^\")\n",
    "    plt.axis(axes)\n",
    "    plt.grid(True, which='both')\n",
    "    plt.xlabel(r\"$x_1$\", fontsize=20)\n",
    "    plt.ylabel(r\"$x_2$\", fontsize=20, rotation=0)\n",
    "\n",
    "plot_dataset(X, y, [-1.5, 2.5, -1, 1.5])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=(('poly_features', PolynomialFeatures(degree=3, include_bias=True, interaction_only=False)), ('scaler', StandardScaler(copy=True, with_mean=True, with_std=True)), ('svm_clf', LinearSVC(C=10, class_weight=None, dual=True, fit_intercept=True,\n",
       "     intercept_scaling=1, loss='hinge', max_iter=1000, multi_class='ovr',\n",
       "     penalty='l2', random_state=None, tol=0.0001, verbose=0))))"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.datasets import make_moons\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "\n",
    "polynomial_svm_clf = Pipeline((\n",
    "        (\"poly_features\", PolynomialFeatures(degree=3)),\n",
    "        (\"scaler\", StandardScaler()),\n",
    "        (\"svm_clf\", LinearSVC(C=10, loss=\"hinge\"))\n",
    "    ))\n",
    "\n",
    "polynomial_svm_clf.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure moons_polynomial_svc_plot\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAagAAAEYCAYAAAAJeGK1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f112dbe0f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_predictions(clf, axes):\n",
    "    x0s = np.linspace(axes[0], axes[1], 100)\n",
    "    x1s = np.linspace(axes[2], axes[3], 100)\n",
    "    x0, x1 = np.meshgrid(x0s, x1s)\n",
    "    X = np.c_[x0.ravel(), x1.ravel()]\n",
    "    y_pred = clf.predict(X).reshape(x0.shape)\n",
    "    y_decision = clf.decision_function(X).reshape(x0.shape)\n",
    "    plt.contourf(x0, x1, y_pred, cmap=plt.cm.brg, alpha=0.2)\n",
    "    plt.contourf(x0, x1, y_decision, cmap=plt.cm.brg, alpha=0.1)\n",
    "\n",
    "plot_predictions(polynomial_svm_clf, [-1.5, 2.5, -1, 1.5])\n",
    "plot_dataset(X, y, [-1.5, 2.5, -1, 1.5])\n",
    "\n",
    "save_fig(\"moons_polynomial_svc_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=(('scaler', StandardScaler(copy=True, with_mean=True, with_std=True)), ('svm_clf', SVC(C=5, cache_size=200, class_weight=None, coef0=1,\n",
       "  decision_function_shape=None, degree=3, gamma='auto', kernel='poly',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False))))"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "\n",
    "poly_kernel_svm_clf = Pipeline((\n",
    "        (\"scaler\", StandardScaler()),\n",
    "        (\"svm_clf\", SVC(kernel=\"poly\", degree=3, coef0=1, C=5))\n",
    "    ))\n",
    "poly_kernel_svm_clf.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=(('scaler', StandardScaler(copy=True, with_mean=True, with_std=True)), ('svm_clf', SVC(C=5, cache_size=200, class_weight=None, coef0=100,\n",
       "  decision_function_shape=None, degree=10, gamma='auto', kernel='poly',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False))))"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poly100_kernel_svm_clf = Pipeline((\n",
    "        (\"scaler\", StandardScaler()),\n",
    "        (\"svm_clf\", SVC(kernel=\"poly\", degree=10, coef0=100, C=5))\n",
    "    ))\n",
    "poly100_kernel_svm_clf.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure moons_kernelized_polynomial_svc_plot\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAxAAAAEYCAYAAADMNRC5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nASEiIiIiIpGpgBARERERkcj+H6axNTvFrlIEAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f112da1c0b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(11, 4))\n",
    "\n",
    "plt.subplot(121)\n",
    "plot_predictions(poly_kernel_svm_clf, [-1.5, 2.5, -1, 1.5])\n",
    "plot_dataset(X, y, [-1.5, 2.5, -1, 1.5])\n",
    "plt.title(r\"$d=3, r=1, C=5$\", fontsize=18)\n",
    "\n",
    "plt.subplot(122)\n",
    "plot_predictions(poly100_kernel_svm_clf, [-1.5, 2.5, -1, 1.5])\n",
    "plot_dataset(X, y, [-1.5, 2.5, -1, 1.5])\n",
    "plt.title(r\"$d=10, r=100, C=5$\", fontsize=18)\n",
    "\n",
    "save_fig(\"moons_kernelized_polynomial_svc_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure kernel_method_plot\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAxAAAAEYCAYAAADMNRC5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nTBkIERERERFJmDIQIiIiIiKSsP8Hz7NSdMHpeJgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f112db9f278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def gaussian_rbf(x, landmark, gamma):\n",
    "    return np.exp(-gamma * np.linalg.norm(x - landmark, axis=1)**2)\n",
    "\n",
    "gamma = 0.3\n",
    "\n",
    "x1s = np.linspace(-4.5, 4.5, 200).reshape(-1, 1)\n",
    "x2s = gaussian_rbf(x1s, -2, gamma)\n",
    "x3s = gaussian_rbf(x1s, 1, gamma)\n",
    "\n",
    "XK = np.c_[gaussian_rbf(X1D, -2, gamma), gaussian_rbf(X1D, 1, gamma)]\n",
    "yk = np.array([0, 0, 1, 1, 1, 1, 1, 0, 0])\n",
    "\n",
    "plt.figure(figsize=(11, 4))\n",
    "\n",
    "plt.subplot(121)\n",
    "plt.grid(True, which='both')\n",
    "plt.axhline(y=0, color='k')\n",
    "plt.scatter(x=[-2, 1], y=[0, 0], s=150, alpha=0.5, c=\"red\")\n",
    "plt.plot(X1D[:, 0][yk==0], np.zeros(4), \"bs\")\n",
    "plt.plot(X1D[:, 0][yk==1], np.zeros(5), \"g^\")\n",
    "plt.plot(x1s, x2s, \"g--\")\n",
    "plt.plot(x1s, x3s, \"b:\")\n",
    "plt.gca().get_yaxis().set_ticks([0, 0.25, 0.5, 0.75, 1])\n",
    "plt.xlabel(r\"$x_1$\", fontsize=20)\n",
    "plt.ylabel(r\"Similarity\", fontsize=14)\n",
    "plt.annotate(r'$\\mathbf{x}$',\n",
    "             xy=(X1D[3, 0], 0),\n",
    "             xytext=(-0.5, 0.20),\n",
    "             ha=\"center\",\n",
    "             arrowprops=dict(facecolor='black', shrink=0.1),\n",
    "             fontsize=18,\n",
    "            )\n",
    "plt.text(-2, 0.9, \"$x_2$\", ha=\"center\", fontsize=20)\n",
    "plt.text(1, 0.9, \"$x_3$\", ha=\"center\", fontsize=20)\n",
    "plt.axis([-4.5, 4.5, -0.1, 1.1])\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.grid(True, which='both')\n",
    "plt.axhline(y=0, color='k')\n",
    "plt.axvline(x=0, color='k')\n",
    "plt.plot(XK[:, 0][yk==0], XK[:, 1][yk==0], \"bs\")\n",
    "plt.plot(XK[:, 0][yk==1], XK[:, 1][yk==1], \"g^\")\n",
    "plt.xlabel(r\"$x_2$\", fontsize=20)\n",
    "plt.ylabel(r\"$x_3$  \", fontsize=20, rotation=0)\n",
    "plt.annotate(r'$\\phi\\left(\\mathbf{x}\\right)$',\n",
    "             xy=(XK[3, 0], XK[3, 1]),\n",
    "             xytext=(0.65, 0.50),\n",
    "             ha=\"center\",\n",
    "             arrowprops=dict(facecolor='black', shrink=0.1),\n",
    "             fontsize=18,\n",
    "            )\n",
    "plt.plot([-0.1, 1.1], [0.57, -0.1], \"r--\", linewidth=3)\n",
    "plt.axis([-0.1, 1.1, -0.1, 1.1])\n",
    "    \n",
    "plt.subplots_adjust(right=1)\n",
    "\n",
    "save_fig(\"kernel_method_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Phi(-1.0, -2) = [ 0.74081822]\n",
      "Phi(-1.0, 1) = [ 0.30119421]\n"
     ]
    }
   ],
   "source": [
    "x1_example = X1D[3, 0]\n",
    "for landmark in (-2, 1):\n",
    "    k = gaussian_rbf(np.array([[x1_example]]), np.array([[landmark]]), gamma)\n",
    "    print(\"Phi({}, {}) = {}\".format(x1_example, landmark, k))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=(('scaler', StandardScaler(copy=True, with_mean=True, with_std=True)), ('svm_clf', SVC(C=0.001, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape=None, degree=3, gamma=5, kernel='rbf',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False))))"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rbf_kernel_svm_clf = Pipeline((\n",
    "        (\"scaler\", StandardScaler()),\n",
    "        (\"svm_clf\", SVC(kernel=\"rbf\", gamma=5, C=0.001))\n",
    "    ))\n",
    "rbf_kernel_svm_clf.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure moons_rbf_svc_plot\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAxAAAAHwCAYAAAA2IolWAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f112dd11ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "\n",
    "gamma1, gamma2 = 0.1, 5\n",
    "C1, C2 = 0.001, 1000\n",
    "hyperparams = (gamma1, C1), (gamma1, C2), (gamma2, C1), (gamma2, C2)\n",
    "\n",
    "svm_clfs = []\n",
    "for gamma, C in hyperparams:\n",
    "    rbf_kernel_svm_clf = Pipeline((\n",
    "            (\"scaler\", StandardScaler()),\n",
    "            (\"svm_clf\", SVC(kernel=\"rbf\", gamma=gamma, C=C))\n",
    "        ))\n",
    "    rbf_kernel_svm_clf.fit(X, y)\n",
    "    svm_clfs.append(rbf_kernel_svm_clf)\n",
    "\n",
    "plt.figure(figsize=(11, 7))\n",
    "\n",
    "for i, svm_clf in enumerate(svm_clfs):\n",
    "    plt.subplot(221 + i)\n",
    "    plot_predictions(svm_clf, [-1.5, 2.5, -1, 1.5])\n",
    "    plot_dataset(X, y, [-1.5, 2.5, -1, 1.5])\n",
    "    gamma, C = hyperparams[i]\n",
    "    plt.title(r\"$\\gamma = {}, C = {}$\".format(gamma, C), fontsize=16)\n",
    "\n",
    "save_fig(\"moons_rbf_svc_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Regression\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "rnd.seed(42)\n",
    "m = 50\n",
    "X = 2 * rnd.rand(m, 1)\n",
    "y = (4 + 3 * X + rnd.randn(m, 1)).ravel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearSVR(C=1.0, dual=True, epsilon=1.5, fit_intercept=True,\n",
       "     intercept_scaling=1.0, loss='epsilon_insensitive', max_iter=1000,\n",
       "     random_state=None, tol=0.0001, verbose=0)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import LinearSVR\n",
    "\n",
    "svm_reg = LinearSVR(epsilon=1.5)\n",
    "svm_reg.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "svm_reg1 = LinearSVR(epsilon=1.5)\n",
    "svm_reg2 = LinearSVR(epsilon=0.5)\n",
    "svm_reg1.fit(X, y)\n",
    "svm_reg2.fit(X, y)\n",
    "\n",
    "def find_support_vectors(svm_reg, X, y):\n",
    "    y_pred = svm_reg.predict(X)\n",
    "    off_margin = (np.abs(y - y_pred) >= svm_reg.epsilon)\n",
    "    return np.argwhere(off_margin)\n",
    "\n",
    "svm_reg1.support_ = find_support_vectors(svm_reg1, X, y)\n",
    "svm_reg2.support_ = find_support_vectors(svm_reg2, X, y)\n",
    "\n",
    "eps_x1 = 1\n",
    "eps_y_pred = svm_reg1.predict([[eps_x1]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure svm_regression_plot\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAoAAAAEYCAYAAADMEEeQAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f112d8e6588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_svm_regression(svm_reg, X, y, axes):\n",
    "    x1s = np.linspace(axes[0], axes[1], 100).reshape(100, 1)\n",
    "    y_pred = svm_reg.predict(x1s)\n",
    "    plt.plot(x1s, y_pred, \"k-\", linewidth=2, label=r\"$\\hat{y}$\")\n",
    "    plt.plot(x1s, y_pred + svm_reg.epsilon, \"k--\")\n",
    "    plt.plot(x1s, y_pred - svm_reg.epsilon, \"k--\")\n",
    "    plt.scatter(X[svm_reg.support_], y[svm_reg.support_], s=180, facecolors='#FFAAAA')\n",
    "    plt.plot(X, y, \"bo\")\n",
    "    plt.xlabel(r\"$x_1$\", fontsize=18)\n",
    "    plt.legend(loc=\"upper left\", fontsize=18)\n",
    "    plt.axis(axes)\n",
    "\n",
    "plt.figure(figsize=(9, 4))\n",
    "plt.subplot(121)\n",
    "plot_svm_regression(svm_reg1, X, y, [0, 2, 3, 11])\n",
    "plt.title(r\"$\\epsilon = {}$\".format(svm_reg1.epsilon), fontsize=18)\n",
    "plt.ylabel(r\"$y$\", fontsize=18, rotation=0)\n",
    "#plt.plot([eps_x1, eps_x1], [eps_y_pred, eps_y_pred - svm_reg1.epsilon], \"k-\", linewidth=2)\n",
    "plt.annotate(\n",
    "        '', xy=(eps_x1, eps_y_pred), xycoords='data',\n",
    "        xytext=(eps_x1, eps_y_pred - svm_reg1.epsilon),\n",
    "        textcoords='data', arrowprops={'arrowstyle': '<->', 'linewidth': 1.5}\n",
    "    )\n",
    "plt.text(0.91, 5.6, r\"$\\epsilon$\", fontsize=20)\n",
    "plt.subplot(122)\n",
    "plot_svm_regression(svm_reg2, X, y, [0, 2, 3, 11])\n",
    "plt.title(r\"$\\epsilon = {}$\".format(svm_reg2.epsilon), fontsize=18)\n",
    "save_fig(\"svm_regression_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "rnd.seed(42)\n",
    "m = 100\n",
    "X = 2 * rnd.rand(m, 1) - 1\n",
    "y = (0.2 + 0.1 * X + 0.5 * X**2 + rnd.randn(m, 1)/10).ravel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVR(C=100, cache_size=200, coef0=0.0, degree=2, epsilon=0.1, gamma='auto',\n",
       "  kernel='poly', max_iter=-1, shrinking=True, tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVR\n",
    "\n",
    "svm_poly_reg = SVR(kernel=\"poly\", degree=2, C=100, epsilon=0.1)\n",
    "svm_poly_reg.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVR(C=0.01, cache_size=200, coef0=0.0, degree=2, epsilon=0.1, gamma='auto',\n",
       "  kernel='poly', max_iter=-1, shrinking=True, tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVR\n",
    "\n",
    "svm_poly_reg1 = SVR(kernel=\"poly\", degree=2, C=100, epsilon=0.1)\n",
    "svm_poly_reg2 = SVR(kernel=\"poly\", degree=2, C=0.01, epsilon=0.1)\n",
    "svm_poly_reg1.fit(X, y)\n",
    "svm_poly_reg2.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure svm_with_polynomial_kernel_plot\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAoAAAAEYCAYAAADMEEeQAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f112daa03c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(9, 4))\n",
    "plt.subplot(121)\n",
    "plot_svm_regression(svm_poly_reg1, X, y, [-1, 1, 0, 1])\n",
    "plt.title(r\"$degree={}, C={}, \\epsilon = {}$\".format(svm_poly_reg1.degree, svm_poly_reg1.C, svm_poly_reg1.epsilon), fontsize=18)\n",
    "plt.ylabel(r\"$y$\", fontsize=18, rotation=0)\n",
    "plt.subplot(122)\n",
    "plot_svm_regression(svm_poly_reg2, X, y, [-1, 1, 0, 1])\n",
    "plt.title(r\"$degree={}, C={}, \\epsilon = {}$\".format(svm_poly_reg2.degree, svm_poly_reg2.C, svm_poly_reg2.epsilon), fontsize=18)\n",
    "save_fig(\"svm_with_polynomial_kernel_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Under the hood"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "iris = datasets.load_iris()\n",
    "X = iris[\"data\"][:, (2, 3)]  # petal length, petal width\n",
    "y = (iris[\"target\"] == 2).astype(np.float64)  # Iris-Virginica"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure iris_3D_plot\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAxAAAAGoCAYAAADW/wPMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f112dcdca90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "\n",
    "def plot_3D_decision_function(ax, w, b, x1_lim=[4, 6], x2_lim=[0.8, 2.8]):\n",
    "    x1_in_bounds = (X[:, 0] > x1_lim[0]) & (X[:, 0] < x1_lim[1])\n",
    "    X_crop = X[x1_in_bounds]\n",
    "    y_crop = y[x1_in_bounds]\n",
    "    x1s = np.linspace(x1_lim[0], x1_lim[1], 20)\n",
    "    x2s = np.linspace(x2_lim[0], x2_lim[1], 20)\n",
    "    x1, x2 = np.meshgrid(x1s, x2s)\n",
    "    xs = np.c_[x1.ravel(), x2.ravel()]\n",
    "    df = (xs.dot(w) + b).reshape(x1.shape)\n",
    "    m = 1 / np.linalg.norm(w)\n",
    "    boundary_x2s = -x1s*(w[0]/w[1])-b/w[1]\n",
    "    margin_x2s_1 = -x1s*(w[0]/w[1])-(b-1)/w[1]\n",
    "    margin_x2s_2 = -x1s*(w[0]/w[1])-(b+1)/w[1]\n",
    "    ax.plot_surface(x1s, x2, 0, color=\"b\", alpha=0.2, cstride=100, rstride=100)\n",
    "    ax.plot(x1s, boundary_x2s, 0, \"k-\", linewidth=2, label=r\"$h=0$\")\n",
    "    ax.plot(x1s, margin_x2s_1, 0, \"k--\", linewidth=2, label=r\"$h=\\pm 1$\")\n",
    "    ax.plot(x1s, margin_x2s_2, 0, \"k--\", linewidth=2)\n",
    "    ax.plot(X_crop[:, 0][y_crop==1], X_crop[:, 1][y_crop==1], 0, \"g^\")\n",
    "    ax.plot_wireframe(x1, x2, df, alpha=0.3, color=\"k\")\n",
    "    ax.plot(X_crop[:, 0][y_crop==0], X_crop[:, 1][y_crop==0], 0, \"bs\")\n",
    "    ax.axis(x1_lim + x2_lim)\n",
    "    ax.text(4.5, 2.5, 3.8, \"Decision function $h$\", fontsize=15)\n",
    "    ax.set_xlabel(r\"Petal length\", fontsize=15)\n",
    "    ax.set_ylabel(r\"Petal width\", fontsize=15)\n",
    "    ax.set_zlabel(r\"$h = \\mathbf{w}^t \\cdot \\mathbf{x} + b$\", fontsize=18)\n",
    "    ax.legend(loc=\"upper left\", fontsize=16)\n",
    "\n",
    "fig = plt.figure(figsize=(11, 6))\n",
    "ax1 = fig.add_subplot(111, projection='3d')\n",
    "plot_3D_decision_function(ax1, w=svm_clf2.coef_[0], b=svm_clf2.intercept_[0])\n",
    "\n",
    "save_fig(\"iris_3D_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Small weight vector results in a large margin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure small_w_large_margin_plot\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA1gAAADfCAYAAADrwnJVAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\niIiI5Mj/B/7Ldc5B003JAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f112dc8ca58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_2D_decision_function(w, b, ylabel=True, x1_lim=[-3, 3]):\n",
    "    x1 = np.linspace(x1_lim[0], x1_lim[1], 200)\n",
    "    y = w * x1 + b\n",
    "    m = 1 / w\n",
    "\n",
    "    plt.plot(x1, y)\n",
    "    plt.plot(x1_lim, [1, 1], \"k:\")\n",
    "    plt.plot(x1_lim, [-1, -1], \"k:\")\n",
    "    plt.axhline(y=0, color='k')\n",
    "    plt.axvline(x=0, color='k')\n",
    "    plt.plot([m, m], [0, 1], \"k--\")\n",
    "    plt.plot([-m, -m], [0, -1], \"k--\")\n",
    "    plt.plot([-m, m], [0, 0], \"k-o\", linewidth=3)\n",
    "    plt.axis(x1_lim + [-2, 2])\n",
    "    plt.xlabel(r\"$x_1$\", fontsize=16)\n",
    "    if ylabel:\n",
    "        plt.ylabel(r\"$w_1 x_1$  \", rotation=0, fontsize=16)\n",
    "    plt.title(r\"$w_1 = {}$\".format(w), fontsize=16)\n",
    "\n",
    "plt.figure(figsize=(12, 3.2))\n",
    "plt.subplot(121)\n",
    "plot_2D_decision_function(1, 0)\n",
    "plt.subplot(122)\n",
    "plot_2D_decision_function(0.5, 0, ylabel=False)\n",
    "save_fig(\"small_w_large_margin_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "from sklearn import datasets\n",
    "\n",
    "iris = datasets.load_iris()\n",
    "X = iris[\"data\"][:, (2, 3)] # petal length, petal width\n",
    "y = (iris[\"target\"] == 2).astype(np.float64) # Iris-Virginica\n",
    "\n",
    "svm_clf = SVC(kernel=\"linear\", C=1)\n",
    "svm_clf.fit(X, y)\n",
    "svm_clf.predict([[5.3, 1.3]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Hinge loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure hinge_plot\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWAAAADCCAYAAAB3whgdAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f112d8b7048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "t = np.linspace(-2, 4, 200)\n",
    "h = np.where(1 - t < 0, 0, 1 - t)  # max(0, 1-t)\n",
    "\n",
    "plt.figure(figsize=(5,2.8))\n",
    "plt.plot(t, h, \"b-\", linewidth=2, label=\"$max(0, 1 - t)$\")\n",
    "plt.grid(True, which='both')\n",
    "plt.axhline(y=0, color='k')\n",
    "plt.axvline(x=0, color='k')\n",
    "plt.yticks(np.arange(-1, 2.5, 1))\n",
    "plt.xlabel(\"$t$\", fontsize=16)\n",
    "plt.axis([-2, 4, -1, 2.5])\n",
    "plt.legend(loc=\"upper right\", fontsize=16)\n",
    "save_fig(\"hinge_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Extra material"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Training time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f112d8d87b8>]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYEAAAEFCAYAAAAFeFvqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nGG0ENBqN5ipGGwGNRqO5ivn/6cWkZfjIdmAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f112d8d8780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X, y = make_moons(n_samples=1000, noise=0.4)\n",
    "plt.plot(X[:, 0][y==0], X[:, 1][y==0], \"bs\")\n",
    "plt.plot(X[:, 0][y==1], X[:, 1][y==1], \"g^\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LibSVM]0 0.1 1.1718690395355225\n",
      "[LibSVM]1 0.01 1.1062052249908447\n",
      "[LibSVM]2 0.001 1.6842045783996582\n",
      "[LibSVM]3 0.0001 1.656536340713501\n",
      "[LibSVM]4 1e-05 1.4322607517242432\n",
      "[LibSVM]5 1.0000000000000002e-06 1.9217710494995117\n",
      "[LibSVM]6 1.0000000000000002e-07 0.9491260051727295\n",
      "[LibSVM]7 1.0000000000000002e-08 1.4430484771728516\n",
      "[LibSVM]8 1.0000000000000003e-09 1.7330551147460938\n",
      "[LibSVM]9 1.0000000000000003e-10 1.1007390022277832\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f112dc33f60>]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYEAAAEJCAYAAAByupuRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nCqYiICJSwVQEREQq2P8HnYKm/YTTNTsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f112d9aa5c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import time\n",
    "\n",
    "tol = 0.1\n",
    "tols = []\n",
    "times = []\n",
    "for i in range(10):\n",
    "    svm_clf = SVC(kernel=\"poly\", gamma=3, C=10, tol=tol, verbose=1)\n",
    "    t1 = time.time()\n",
    "    svm_clf.fit(X, y)\n",
    "    t2 = time.time()\n",
    "    times.append(t2-t1)\n",
    "    tols.append(tol)\n",
    "    print(i, tol, t2-t1)\n",
    "    tol /= 10\n",
    "plt.semilogx(tols, times)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Linear SVM classifier implementation using Batch Gradient Descent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# Training set\n",
    "X = iris[\"data\"][:, (2, 3)] # petal length, petal width\n",
    "y = (iris[\"target\"] == 2).astype(np.float64).reshape(-1, 1) # Iris-Virginica"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.],\n",
       "       [ 0.]])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.base import BaseEstimator\n",
    "\n",
    "class MyLinearSVC(BaseEstimator):\n",
    "    def __init__(self, C=1, eta0=1, eta_d=10000, n_epochs=1000, random_state=None):\n",
    "        self.C = C\n",
    "        self.eta0 = eta0\n",
    "        self.n_epochs = n_epochs\n",
    "        self.random_state = random_state\n",
    "        self.eta_d = eta_d\n",
    "\n",
    "    def eta(self, epoch):\n",
    "        return self.eta0 / (epoch + self.eta_d)\n",
    "        \n",
    "    def fit(self, X, y):\n",
    "        # Random initialization\n",
    "        if self.random_state:\n",
    "            rnd.seed(self.random_state)\n",
    "        w = rnd.randn(X.shape[1], 1) # n feature weights\n",
    "        b = 0\n",
    "\n",
    "        m = len(X)\n",
    "        t = y * 2 - 1  # -1 if t==0, +1 if t==1\n",
    "        X_t = X * t\n",
    "        self.Js=[]\n",
    "\n",
    "        # Training\n",
    "        for epoch in range(self.n_epochs):\n",
    "            support_vectors_idx = (X_t.dot(w) + t * b < 1).ravel()\n",
    "            X_t_sv = X_t[support_vectors_idx]\n",
    "            t_sv = t[support_vectors_idx]\n",
    "\n",
    "            J = 1/2 * np.sum(w * w) + self.C * (np.sum(1 - X_t_sv.dot(w)) - b * np.sum(t_sv))\n",
    "            self.Js.append(J)\n",
    "\n",
    "            w_gradient_vector = w - self.C * np.sum(X_t_sv, axis=0).reshape(-1, 1)\n",
    "            b_derivative = -C * np.sum(t_sv)\n",
    "                \n",
    "            w = w - self.eta(epoch) * w_gradient_vector\n",
    "            b = b - self.eta(epoch) * b_derivative\n",
    "            \n",
    "\n",
    "        self.intercept_ = np.array([b])\n",
    "        self.coef_ = np.array([w])\n",
    "        support_vectors_idx = (X_t.dot(w) + b < 1).ravel()\n",
    "        self.support_vectors_ = X[support_vectors_idx]\n",
    "        return self\n",
    "\n",
    "    def decision_function(self, X):\n",
    "        return X.dot(self.coef_[0]) + self.intercept_[0]\n",
    "\n",
    "    def predict(self, X):\n",
    "        return (self.decision_function(X) >= 0).astype(np.float64)\n",
    "\n",
    "C=2\n",
    "svm_clf = MyLinearSVC(C=C, eta0 = 10, eta_d = 1000, n_epochs=60000, random_state=2)\n",
    "svm_clf.fit(X, y)\n",
    "svm_clf.predict(np.array([[5, 2], [4, 1]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 60000, 0, 100]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYoAAAEFCAYAAAD9mKAdAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nI4NCktTIoJAkNTIoJEmN/j9s5sK29URsjQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f112d83c390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(range(svm_clf.n_epochs), svm_clf.Js)\n",
    "plt.axis([0, svm_clf.n_epochs, 0, 100])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-15.56780998] [[[ 2.28129013]\n",
      "  [ 2.71597487]]]\n"
     ]
    }
   ],
   "source": [
    "print(svm_clf.intercept_, svm_clf.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-15.51721253] [[ 2.27128546  2.71287145]]\n"
     ]
    }
   ],
   "source": [
    "svm_clf2 = SVC(kernel=\"linear\", C=C)\n",
    "svm_clf2.fit(X, y.ravel())\n",
    "print(svm_clf2.intercept_, svm_clf2.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[4, 6, 0.8, 2.8]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAt8AAAD0CAYAAABD/koYAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\niqIoimIl/w86XTx9vEX6nQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f112d804fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "yr = y.ravel()\n",
    "plt.figure(figsize=(12,3.2))\n",
    "plt.subplot(121)\n",
    "plt.plot(X[:, 0][yr==1], X[:, 1][yr==1], \"g^\", label=\"Iris-Virginica\")\n",
    "plt.plot(X[:, 0][yr==0], X[:, 1][yr==0], \"bs\", label=\"Not Iris-Virginica\")\n",
    "plot_svc_decision_boundary(svm_clf, 4, 6)\n",
    "plt.xlabel(\"Petal length\", fontsize=14)\n",
    "plt.ylabel(\"Petal width\", fontsize=14)\n",
    "plt.title(\"MyLinearSVC\", fontsize=14)\n",
    "plt.axis([4, 6, 0.8, 2.8])\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.plot(X[:, 0][yr==1], X[:, 1][yr==1], \"g^\")\n",
    "plt.plot(X[:, 0][yr==0], X[:, 1][yr==0], \"bs\")\n",
    "plot_svc_decision_boundary(svm_clf2, 4, 6)\n",
    "plt.xlabel(\"Petal length\", fontsize=14)\n",
    "plt.title(\"SVC\", fontsize=14)\n",
    "plt.axis([4, 6, 0.8, 2.8])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-14.062485     2.24179316   1.79750198]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[4, 6, 0.8, 2.8]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD0CAYAAACLpN0/AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsnXdYVMf3h98BBAXFAoq9ixoLFqJo7DGWxJ8aTRRQE8Fv\n1BhLEkVjLGCNLUajEkssURSNGhPTrFHAhhU0GsWCNSpiRVDazu+PBbI0WdiFu8B9n2cedu/O3Pnc\nC5ydO3POHCGlREVFRUUl/2OmtAAVFRUVFeOgGnQVFRWVAoJq0FVUVFQKCKpBV1FRUSkgqAZdRUVF\npYCgGnQVFRWVAoJq0FVUdBBCfCiEiMrD/jRCiD467+sKIY4IIV4IIa5lVEdFJTNUg66S5wgh7IUQ\nvkKIcCHESyHEPSHEXiHEmzp1agghvhdCXE+qc1sIsV8I8YEQoohOPY1OiRZCXBVCbBRCvJFJ332S\nzvNYCPFcCBEqhJgphCirUy0vgzPKA7/qvJ8JRAOOwOuZ1FFRyRDVoKsowU+AM+AB1AHeAf4E7ACE\nEM7AGeA14BOgIdAO8AU+SGqryxC0Rq8e4AnEAYFCiLG6lYQQs4AfgdNJfdYHxgDVgOFGvka9kFJG\nSCnjdQ7VBg5JKW9JKR9mUidbCCHMDdWpkk+QUqpFLXlWgJKABuj0ijrngWA9z6cB+mRwfBZaw14z\n6X2LpLpjMjmPbdLPD4FnOsdrAj8Dd4HnwCngnTRt+wChQAzwEDgAlE36rHJS+4doR94XgH4Z6U96\nnajzc2pG1whUBDYDj5LKb0Btnc+9gXNJ13IFiAeslf7dqyX3izpCV8lrnieVnkIIq7QfCiGaoB05\nLzCwn68Bc6B30vsBSf0uzaiylPJZJucpDvwBvAk0BrYB24UQjkl6HQB/YC3aJ4S2wAad9t8BxYD2\naJ84PgWeZNJXeSAM7bVXIIN7IIQohvYLIzqpLxfgX2CfEKKoTtUagBvwHuAEvMykT5UChIXSAlQK\nF1LKRCHEh8AqYJgQ4gxwGNgqpTyOdu5YojVsAAghbIE7OqeZJaWck0U/j4QQEWhH2KCdyrgqpUzM\npt6zwFmdQ18JIXqiNZSz0Y6WLYDtUspbSXUu6NSvCmyTUv6d9P7GK/qKEEIkAM+llBGZVHNLqjsk\n+YAQ4mPgPtAD7RcOQBFgoJQyMuurVCkoqCN0lTxHSrkDrSHsgXb02wo4JoSYmEmTKLSjTCe0o1FL\nPbsS/LfAKXKiVQhhLYSYJ4Q4L4R4lOQB0xytoQbtVMt+4LwQYpsQYrgQwl7nFIuBKUmeKzOEEM1y\nokOHZkBNIURUckE74i8F1NKpd1s15oUP1aCrKIKUMk5KuV9KOVNK2QZYjXbuNxyt8a2nU1dKKa9J\nKa+hnRfPEiGEHVAWuJp0KAyoJYTI7lPp10BfYBLahVkn4ARJXypSSo2UsgvwFlrjPgS4LIRolPT5\nGqA6sAbtAvARIcTUbGrQxQztgnFj/vuSc0L7ZLNCp160AX2o5FNUg65iKvyDduriYtLr8UIIQ/4+\nx6FdWNyZ9H4TYAOMzKiyEKJkJud5A1gvpfw5adrkX1KPhAGQUgZLKWdIKV9PqtNf57N/pZTfSyld\nganA0BxeE2g9dGoDD5O/5HRKZnPzKoUEdQ5dJU8RQpQBtqIdsZ5FO53yOuAF7JNSRgkhBgN70Y5m\nZ6M18OZAG6ASWkOtS6mkxUlLtMZ2MDAQ8Eoa1SOlPC6EmA/MF0JUAbYDt9HOsXsCl4EZGUgOA94V\nQuwEEtAa5JTFXCFES6AzsBvtPHYztJ4t55M+X4TWJTMMrYdPt+TPcshGYCzwixDCG7iJdvqnJ/Cd\nlPLqqxqrFGxUg66S1zwHjgKj0Y40rdAuePqhdTVESnkiaa55IvAtWu+PF2i/AL5EOz2TjES7wAoQ\ni9a98BjQXkp5WLdjKeUXQogTaH3bPdH+/YcDv6D1cc+Iz4HvgUDgMbAIHYMOPEU7ih+Jdh77FjBd\nSumf9LlZ0jVUQfvltR+tQdbVzyvepzompXwhhGgHzEHrU18S7RPBgSR9KoUYIaWasUhFRUWlIKDO\noauoqKgUEFSDrqKiolJAUA26ioqKSgGhQCyKCiHUhQAVFZUCgZQyR0FwUIBG6Mmb00RHRzNs2LCU\n4z169CAiIkLxTXOULLt378bBwYH69esTHR2NlBJvb2/FdeW3ot4z9X7ldjGUAmPQk7G2tmb58uVs\n376dUqVK8dtvv+Hk5MT+/fuVlqYYXbp04ezZs+zYsQNra2ul5aioqOQSBc6gJ9OnTx9CQ0Np27Yt\nd+/e5a233mLixInEx+d4W+l8Tbly5ahbt67SMlRUVHKRAmvQAapWrcpff/2Fj48PQgjmzJlDmzZt\nuHbtmtLSFKdDhw4AREdH4+fnZ5THvYJO8j1T0Q/1fuU9BSKwSAghs7qOoKAgBgwYwK1btyhRogQr\nVqzAzc0tjxSaLkOGDGHNmjX06dOHVatWUaZMGaUlqagUWoQQSHVRNGvatm1LaGgoffv2JSoqCnd3\ndzw8PHj+/LnS0hSlc+fOlChRgp9++okmTZoQFBSktCQVFZUcUmgMOkDp0qXZunUrK1asoFixYqxb\nt45mzZpx6tQppaUphpubGyEhIbRs2ZJbt27RoUMHfHx81CkYFZV8SKEy6KB9pBk6dCgnT56kUaNG\nXL58mVatWvH111+j0WiUlqcINWvWJCgoiIkTJyKl5MaNGwiR46c+FRUVhSg0c+gZ8fLlS7y8vFi6\nVJtmslu3bqxbtw4HBwdjS8w3BAQE0Lx5c4oXL660FBWVQoehc+iF2qAn88svv+Dp6cmjR49wcHBg\n/fr1dOnSxYgKVVRUVLJGXRQ1Ar169eLs2bN06NCB+/fv07VrV7y8vIiL0yvbWaHg/PnznD17NuuK\nKioqiqEa9CQqVarEvn37mDVrFubm5ixYsIDWrVtz+fJlpaUpTkxMDP369aNFixYsW7ZMXTBVUTFR\nVIOug7m5OV9++SVBQUFUr16dU6dO0bRpU9avX1/ojVibNm2IjY1l5MiR9O7dm8hINaG8ioqpoc6h\nZ8LTp08ZNmwYW7ZsAWDAgAH4+vpia2tr1H7yE9u2beOjjz7iyZMnVKxYET8/Pzp27Ki0LBWVAoM6\nh55LlCxZEn9/f1avXo21tTUbN26kadOmHD9+XGlpivHee+8REhLCG2+8wb///svNmzeVlqSioqKD\nOkLXg0uXLuHq6kpISAgWFhbMnDkTLy8vzMwK5/dhQkICO3bs4L333lP91VVUjIjqtkjuG3SA2NhY\nvvjiCxYtWgRoQ+bXr19PhQoVcrVfFRWVwoM65ZJHWFlZ8c033/D7779TtmxZ9u3bR+PGjfn999+V\nlmZSBAQEFPr9cVRUlEI16Nnk7bffJjQ0lM6dOxMZGUmPHj349NNPiY2NVVqa4ly4cIHu3bvTvHlz\nzpw5o7QcFZVCh2rQc0CFChXYvXs3c+fOxcLCgsWLF+Pi4sLFixeVlqYoQghq1apFWFgYLi4uLFq0\nqNC7e6qo5CXqHLqBHD9+HHd3d65evYq1tTXffvstnp6ehXax8MWLF4wbNw5fX18Aunfvzrp16yhX\nrpzCylRUTJ98NYcuhLAUQnwvhLguhHgqhDglhOiWSd0PhRAJQohnQoiopJ/t8lKvPrRo0YLTp08z\ncOBAYmJi+N///oerqytPnjxRWpoiFCtWjGXLlrFjxw7KlClDQEAADx8+VFqWikqhIE9H6EIIa2Ac\nsFZKeUsI8Q7gDzSUUt5MU/dDYIiUMksjruQIXZcNGzYwYsQInj9/TrVq1di0aROtW7dWWpZi3L59\nm9DQUN555x2lpaio5Avy1QhdShkjpZwupbyV9P53IBxonpc6cotBgwZx5swZnJ2duXHjBu3atWPW\nrFkkJiYqLU0RKleurBpzlTxHSskX077Id+s3xtCr6KKoEMIBqAOcz6RKUyFEhBDiohBishDC5Bdx\na9euzeHDh/Hy8iIxMZHJkyfTuXNnbt++rbQ0k0FKSWBgoNIyVAoo23/dju9fvvz0209KS8kW23/d\nbvhJpJSKFMAC2Av4ZvJ5daBa0usGaI3+hEzqSlNk9+7d0sHBQQKyTJky8ueff1Zakkmwdu1aCchB\ngwbJZ8+eKS1HpQCh0Whky/daSryRLd9rKTUajdKS9CJFt9aW5diuKjLiFVoXED8gFhiVUR0p5XUp\n5Y2k1+eB6cB7mZ3Tx8cnpRw8eND4onNAly5dOHv2LN26dePRo0f07t2bTz75hBcvXigtTVGEEBQr\nVowNGzbQrFkzTp48qbQklQLC9l+3c67EORBwrvg5kx+lHzx4EB8fH/q79+fUFSPkNjbk2yCnBVgD\n7AMss9GmP3Ayk8+M8B2ZeyQmJsqFCxfKIkWKSEA2bNhQ/v3330rLUpQLFy5IJycnCUgLCws5b948\nmZiYqLQslXyM7ugcH/LNKD2V7vw2QhdCLAfqAT2llJmmBBJCdBNClEt6XQ+YDPycNyqNi5mZGZ99\n9hnHjh2jTp06/P333zg7O7NixYp8t3BjLOrXr8+xY8cYPXo0CQkJ/PDDD2q0rYpB6I7OgXwzSk+n\n2wDy2m2xKnAdeAkku35IYBhwCLgA1JdS3hZCzAcGATbAfWADMFNKmc5lxFTcFvXh+fPnjB49mrVr\n1wLQp08fVq1aRZkyZRRWphy//fYb1apVo1GjRkpLUcnHfDb1M07fOJ0qqE9KSbNqzfhm+jcKKns1\nuroDfghQd1vMTwY9GX9/f4YPH86zZ8+oXLkyGzdupF07k4ubUlFRyUPylR+6yn+4ubkREhKCi4sL\nt2/fpmPHjvj4+JCQkKC0NJMhKiqKsLAwpWWoqOQbVIOuIDVq1CAwMJAvv/wSKSXTpk2jQ4cO3Lhx\nQ2lpJsHIkSNp1qwZ69aty9drDTKfBrooiUajweUtFzQajdJS8hWqQVeYIkWKMGvWLPbt20eFChU4\nfPgwTZo0Yft2IwQZ5GPi4+OJj48nOjoaDw8P3N3defr0qdKyckR+DXRREi9vL4KfBDPBZ4LSUvIX\nhrjImErBxN0W9eXBgweyR48eEu1CsRw6dKiMjo5WWpZiaDQauW7dOmljYyMBWb16dXn06FGlZWWL\n/BrooiSJiYnSprGNxBtp09imULmzkt/cFlUyx97enp07d7JkyRKsrKxYuXIlzs7OnD17VmlpiiCE\n4MMPP+T06dM0a9aM69evs2bNGqVlZYv8FuhiCnh5exHdMBoERDeMVkfp2UD1cjFRQkNDcXV15eLF\ni1hZWbFgwQI++eSTQrvPemxsLAsXLmT06NHY2NgoLUcvpJS06teK4AbBWh9jCS3Pt+Toj0cL7e8x\nKzQaDbZNbYl+NzrlntnssOHZmWeFIim76uVSQHFycuLUqVMMHTqU2NhYRo0aRa9evYiMjFRamiJY\nWVkxceLEfGPMIf8GuiiJ7ugcUEfp2UQdoecDtm3bxkcffcSTJ0+oWLEifn5+dOzYUWlZJsP169cp\nX748RYsWVVpKKvJroIuSNO3clGtR19Lds5olanJmX8HPU2voCF016PmEGzduMGDAAA4fPowQgokT\nJ+Lj40ORIkWUlqYo0dHRNG/eHCsrK/z9/XnttdeUlqSikmPUKZdCQrVq1Th48CBTp05FCMHs2bNp\n164d4eHhSktTlLt375KYmMjZs2dxdnZm1apVqr+3SqFFNej5CAsLC6ZNm8aBAweoXLkyx44do0mT\nJmzevFlpaYpRu3ZtTp8+zQcffMCLFy8YOnQo/fr14/Hjx0pLS0EaEFhkSFul+1YKJXUrfs8M8Xk0\nlUIB8UPPDg8fPpTvvvtuis+6h4eHjIqKUlqWovj5+ckSJUpIQG7ZskVpOSls/WWrLNGuhNy2c1ue\ntlW6b6VQUrehfWOgH7rixtgYpTAadCm1QSvfffedLFq0qASko6OjPH36tNKyFOXKlSty1qxZSstI\nwZDAIkODkpTsWymU1G2Mvg016OqUSz5GCMHw4cM5ceIEDRs2JCwsDBcXFxYtWpT8RVfoqFWrFl9+\n+aXSMlIwJLDI0KAkJftWCiV1m8I9KzAG/fvvvy+0Rqxhw4YcP36cESNGEBcXx2effcY777xDRESE\n0tJMips3b+Zpf1JKFmxYQEzVGABiqsUwf/18vf5ODWmrdN9KoaRuU7lnBcagf/TRR/Tv358nT54o\nLUURihUrxrJly9ixYwdlypThzz//pHHjxuzdu1dpaSbB33//Td26dRkxYkSe5XQ1JLDI0KAkJftW\nCiV1m8o9s8jT3nKR4sWLs3XrVoKDg/H396d169ZKS1KE3r174+zszMCBAwkICKBLly54eXkxc+ZM\nLC0tlZanGOfOnUOj0fDdd98RGBjI5s2badiwYa72efjkYZwTnRHhqYNkDp04RN//65trbZXuWymU\n1G0y98yQCXhTKYC8fPmydHZ2loA0NzeXe/fuzdZiREEjISFBzpgxQ5qbm0tAOjs7y8uXLystS1HO\nnDkj69atKwFZtGhR6evrm28W+1QKBxi4KFqgIkXj4uKYMmUKBw8eJCgoqFCPSJM5fPgw7u7u3Lx5\nk+LFi/Pdd98xcOBApWUpRnR0NGPGjGH16tVYW1tz8eJFqlSporQsFRXA8EhRxUfXxiikcVuMjY3N\n/ldjAebx48fy/fffT/FZHzRokHz27JnSshRly5Yt0s/PT2kZKpmg0WjkBJ8Jef4EpVS/yaD6oRde\nP/TsoNFo5Pfffy+tra0lIGvVqiWPHz+utCwVlQxRKjhI6WAqQw263l4uQghrIURrIURvIUQf3ZLj\nx4M85NGjR3Tv3p2///5baSmKIIRgyJAhnDp1CicnJ65evUrr1q2ZP3++mrdRByml6u6pMFJqXQCj\nOkblqeufUv0aE70MuhCiM3ADOAT8BGzTKVtzTZ0RmTZtGrt27eL1119n+fLl+fKXZQzq1avHsWPH\nGDNmDAkJCYwfP55u3bpx7949paWZBGvWrMHR0ZEff/xRaSmFFqUCdEwhMMhg9BnGA+eBdUBFQx4H\ncqugx5RLVFSU9PDwSJlHfvfdd+XDhw+zbFeQ+fXXX6W9vb0EZNmyZeUff/yhtCTFcXd3T/kbGTJk\niHz+/LnSkgoVuuHz+JBnIfxK9ZsW8mjKpTowQ0r5r3G/TvKO4sWLs2bNGvz9/bG1tWXHjh00adIk\n32aSNwY9evQgNDSUN998kwcPHvD222/z+eefExsbq7Q0xfDz82PZsmVYWVmxevVqmjdvTkhIiNKy\nCg1KBeiYSmCQoejltiiE2AMsklL+kfuSsk92E1yEh4fj7u6Oi4sL33yjZo7RaDTMnz+fyZMnk5CQ\nQNOmTfH396du3bpKS1OMc+fO4ebmxvnz53F2dub48eNqHtA8QKksT6aSXSrXMhYJIZrpvK0OzAQW\nAueAeN26UsrTORVgDHKSsSg+Ph4ppeqrrkNwcDBubm6Eh4djbW3N0qVLGTx4cKE1ZDExMUyYMIGP\nP/5YzYSkkifkpkHXoJ1LzOrkUkppnlMBxqAwpKDLK549e8bw4cPx9/cHwNXVleXLl1OyZEmFlamo\nFHxyMwVdDaBm0s9XlZo57dwUOXr0KMOHDycmJkZpKYpga2vLxo0bWbduHTY2NmzevJkmTZpw7Ngx\npaWZFNHR0cTHx2ddMR8jpZppKT/1nSIgqwK0AywyOG4BtDNkVdYYBSMFFiUkJMh69epJQNavX1+G\nhIQY5bz5lUuXLslmzZql7I8za9YsmZCQoLQsxdFoNNLd3V22aNFCXr16VWk5uYaaaSn75IuMRUAi\nUC6D43ZAoiECjFGMZdCllDI0NFS+9tprEpBWVlby22+/LdQbOMXGxsqxY8emuPJ17NhR3r59W2lZ\nihIRESGrVq0qAVmiRAm5ceNGpSUZHTXTUvbJTxmLRNI/dFrsgOjsPxeYLo0bN+bEiRMMGzaM2NhY\nRo8ejYeHh9KyFMPS0pIFCxawa9cuypUrx4EDB3BycuLXX39VWppilC1blpCQEPr27UtUVBQDBgxg\n8ODBREVFKS3NaKiZlrKPSQQmvcraAzuTSiKwW+f9TuB3tNGjuwz5RjFGIZf2ctm2bZssXbq03LFj\nR66cP79x79492bVr15TR+siRI+WLFy+UlqUYGo1Grly5UhYrVkwCctKkSUpLMgqGBNkYGqCjZN+G\nYKy+yeUR+sOkIoDHOu8fAreB5UCB3Yu1b9++XLt2jd69eystxSRwcHDgjz/+YMGCBRQpUoSlS5fS\nsmVLLly4oLS0TImJiWHNmjUMdHWlZ/fuDHR1Zc2aNXover+qvRCCjz76iJMnT/L++++ny2VqaN9K\noWZayj6mEpikb2CRN7BASmmS0yuq22Lec+rUKVxdXbly5QrFihVj8eLF/O9//zMZn/XExERmTJvG\n0iVLaOXoSF9nZ0rb2PA4OprtJ09yNCyMkaNGMcXbG3Pz9F63hrQ3tG+lMSTIxtAAHSX7NgRj9Z1r\nfui5gRDCEvAFOgOlgSvAJCnlrkzqfwaMB4oC24GPpZTpfMWUMOhLly7Fzs4ONze3PO3XlIiKimLU\nqFH88MMPgPaJZtWqVZQuXVpRXYmJibj370/E5cusGTqUGuXKpasTHhGB58qVODg6snHz5lSG1ZD2\nads6lCyJtZWV3n2rFG5yzQ9dCBEuhLimT8lGfxbATaCtlLIkMBX4UQhRNYP+u6I15h3RRqrWAqZl\no69cIywsjM8++wx3d3c8PDx4/vy50pIUoUSJEqxbt46NGzdSokQJtm/fjpOTE4cOHVJU14xp04i4\nfJldEyZkaIwBapQrx64JE7gfFsaMadOM1l63bVlbW5pNmMC49euJS0jQq28VFUN4VaToWJ23xYHP\ngePA0aRjrYAWwNdSyuk5FiBEKOAjpdyR5vhGIFxKOTnpfSdgo5SyQgbnyNMRupSSlStX8umnn/Ly\n5Uvq1KnD5s2badasWdaNCyjXrl3Dzc2N48ePY2Zmhre3N5MmTcrz0WdMTAxVK1Xi5MyZVNcxxlJK\nJu7YxFfvuqd6LA6PiOD1KVO4efs21tbWBrUHUrX9/fRpes2bR6JGQ/nSJQnwnoZjxYqZ9p0RUkom\nTp/IV1O/MpnpLJXcI9dG6FLKr5ML2ojQuVLKt6SUU5PKW8AcwDGnnQshHIA6aLfnTUsDIFTnfShQ\nTgih7PM82ps+bNgwTp48SaNGjbh8+TIuLi5s3ZovtobPFWrWrMmhQ4f44gttlJy3tzedOnXi1q1b\neapj8+bNtHJ0TGWMAbafDsb35h5+OhOc6niNcuVwSfpCNrR92rbvNGvGoenTKVvSlnuPn+Lk5cUP\nBw8me2al6zsjtv+6Hd+/fPPdrn8qyqCvH3ofIKMd/7cCPXPSsRDCAvAD1kkpwzKoUhzQ3dv2Kdo1\n5BI56S83aNCgAcHBwXzyyScUL16cFi1aKC1JUYoUKcJXX33Fnj17KF++PIGBgTg5OfHTT3lnjP7a\ns4e+zs6pjkkpWXB8J1FvvWB+8E7SPs31bd6cv/bsMbh9Rm1b1qlD1cZ20ABexscz2NeXI5cuZdh3\nWqTM/xl0VPIWCz3rRQMd0C5i6tIByLYPltA+O/oBscCoTKo9B2x13tui9X/OMHrDx8fnP1EdOtCh\nQ4fsysoRxYoVY+nSpUyZMgUHB4c86dPU6dy5M2fPnsXDw4Pff/+dvn37MmzYMBYuXJjp1IKxePb0\nKaWrpl6S2X46mHOVb2ldySrf4qczwfRt5pLyeWkbG6KuXze4vZQyw7b/VP8XOoGlrQWdYhvwRr16\nGfadlowCVfr+X9+c3BYVE+XgwYMcPHjQaOfT16B/AywTQjgDybs0uQAfAj456Hc1YA+8LaVMzKTO\necAJbZo7gCbAfSnl44wq6xp0JVCNeWrKli3Lr7/+ypIlS/Dy8mLFihUEBQWxefNmGjVqlGv92pYs\nyePo/7xrk0fXMe20STtiasYyP3AnfZq2TJmTfhwdTQlbW6O0z7StgLguCTwOjEZKmWFbXZJH5zEN\ntOOlmGoxzF8/nz49+qhz6QWItIPPaQYukus15SKlnAcMAhqh3RN9YdLrD6WUc7PToRBiOVAP6Cml\njHtF1fXAECFE/aR580nA2uz0pTQajQYvLy+uXEn7YFM4EEIwevRogoODqVu3LhcuXOD111/H19c3\n16YPOnXpwvaTJ1Pe646utaL+G2Wn1Dl1ik5dumTYflPwIc5UuJ6q/ZkK1/E/fihde0PaxsWl/lcw\nlUAVlfxFXvuhVwWuAy/RbicA2mmUYWgTUJ8HXpNS3k6q/ynwBVo/9G2YkB+6PqxYsYLhw4dTvHhx\nfH19GTRokNKSFCM6OpoxY8awevVqAHr16sXq1auxs7Mzaj/JXionZs6kRrlyfLZ9Haefhafa1F8C\nzWxr8E3fwZl6uRybPh2/wEDm/vUL1hUtKWtri4WZOQmaRB48e0bMv3FM6NSLge3a4eLtncrLJbtt\nf//zT9577z2+++47evToAZhOBh2VvCVfBRblFqZq0J88ecLw4cPZsmULAAMHDmTZsmXYZvCIXVj4\n8ccfGTp0KE+fPqVSpUr4+fkZfb3DZ+pUAn75hV0TJmBVpEim9WLj4+k6Zw4devfGZ/p/nrdTJ09m\n7fLl1HZwYM2IEZkHFvn6cuX+fTyGD2f6zJkATP7yS9YuX45jhQpZtg27exeP4cN5/PQpvr6+AIwe\nPZp58+ZhlSYYSaVwkJuBRc+EEPZJr6OS3mdYctp5QadUqVL4+/uzevVqrK2t8fPzo2nTplzPZBGs\nMNCvXz9CQkJo1aoVd+7coVOnTkyZMoUEncAbQ5ni7U25OnXoNncu4RERGdYJj4ig65w5lK9blyne\n3uk+r2Jvz65Jk14dWDRpElXs7VMdP3L4MJXt7PRqW9nOjiOHD7NkyRLmzZuHhYUF3377LS1btuTi\nxYvZvGoVlVcHFn0IbJZSxgohBpPx9rkASCl/yB15+mGqI3RdLl26hKurK1ZWVgQFBVHkFSPHwkBC\nQgLTp09n5syZSClp1aoVmzZtonr16kY5f8p+KkuX4lKnDn2bN/9vP5VTpzh2+TKjRo1i8tSpqYKf\nDAksSm7uVxanAAAgAElEQVR7YeHCVG01Gg2t503hyPgZmJmZpWrb4PPPuXnnDvb29pw4cQI3Nzeu\nXr1KuXLluH79OsWKFTPoPqiBSfkLdcqF/GHQAWJjY3n8+DHly5dXWorJcPDgQQYOHMidO3coWbIk\nK1eupF+/fkY7f0xMDJs3b+avPXuIevaMEra2dOrSBVdX1wxdKNesWcOOFSv4ddy4VMe3nTqGZ9B3\nrG33cSqXRYAeCxbQZ9gwgoKCuHXqFPumTEn1+dit61kY/hvjavZg/nsfpPrszenTqerszNq12vX+\nqKgoPvnkEzp37swHH6SumxO27dyG59eerB23VnV5zAfkiUEXQkwEDgAnXuFmqBj5xaCrZMzDhw8Z\nMmQIv/zyCwBDhgxh8eLF2NjY5LmWga6udLazY7DOvL6UklbLJxHc7gotA2tzdPisVKPdtQcOsP/R\nI06dPMmErl1TtdVoNNhO/5Do92Ox2WrFs6k/pBqlrz1wgHl79vBPLnhCSSlp1a8VwQ2CaXm+JUd/\nPKqO0k2c3EwSrcs7QADwRAixWwgxUQjRSgihbhNnBCIjI5k6dSqxsbFKS1EEOzs7duzYwbJly7Cy\nsmL16tU0b96ckJCQPNfy7OlTSqf5IskosEiX0jY2RD17RuzLl+naem33I7qJ1g892imWCT/5pWsb\np+fvXUpJYqL+4ymTyKCjkqfo64feBiiFdguAE2gN/AG0Bj7DrW9V9Gf48OHMmDEDFxcXLumEhRcm\nhBCMGDGCEydO0KBBAy5dukTLli1ZvHhxnoa8ZxpYVFMnsChN+H9ycJBV0aKp2mo0Glac36vdrQjA\nEb77ey8ajSZVW0s9PVpWrVrFm2++ye0kF8lXkRKYVDV1YJL6JFuw0XeEjpTyhZRyL7AUWIbWL7wo\n0C6XtBUaxo8fT82aNQkJCaFZs2asWbOm0P7jNWrUiOPHjzN8+HDi4uL49NNP6dy5My1btqSCnR0O\npUtTwc6ONm3acPPmzSzPFxkZiYeHB/Vr16Zm5crUr10bDw8PIiMjM6xvSGCSS9u2+AUFpRz/dMsP\nKaPz5LbRTrF8vvU/HwK/oCBc2rbN8jri4uKYM2cOAQEBODk5pUxPZYYamFQ40XcO/X20+5J3BKqi\n3UY3ADgIHJVSKjpXUBDm0J89e8bHH3/Mpk2bAHB1dWXjxo2p5lsLG5s3b2bAgAFoNBosLSwY8/bb\nvFG3Lo+jo/ELDOTwpUtUrlyZs+fPp/MGiYuLo3uXLhw9epTWdesysG3bFC8Xv6Agjly6RKtWrfhz\nzx4sLS1T2hkSmJTc9uyCBfgFBjLz4E9QDiwtzBEIJJK4hESIgMkd+jCwXTsajxuX4uWSFREREQwe\nPJg///wTgBEjRrBgwYIMPWHUwKT8SV4timqAB8DXwFIppUklRSwIBh20/3AbNmxgxIgRDB8+nAUL\nFigtSTFevHhBlQoVqFK6NEXMzTlx9SpCCMb37MmM/v0pYmFBeEQEbosXczUigpt376YYtri4OF5z\ndKSspSWbxozJNLjHffFiIuPiOB8WlsqoGxKY1Kl9ey6dO6d3YFHdRo34KyBA7/ui0Wj49ttvGT9+\nPPHx8bi5uaUMAlTyP3ll0D8C2ieVEkAQ2tH5AeCM0ta0oBj0ZK5evUqVKlVSGZnCRp2aNbGzsCDA\nxwcLc3Nm/fQT07ZuRSMlLWrXZtPo0dQqX57Y+Hja+/jwMCGBy9e0ybPe7NCBmH//5aCPT5YGuYOP\nD9YVK7JfZ8c7fVPQeaxYQfm6dVOlkZsyaRJ7fvyRwGnTsuy7nbc3Xfr1Y8asWdm+P2fOnGHIkCFs\n2rSJejq7N6rkb/LcD10IURvttrlvAe8Cz6WUZXIqwBgUNINe2Ll58yZ1a9fmn2++oXq5cgxdsZew\nfy14EnOXszd3IeVLzM2K0K5+G/7y/pjwiAhe++wzLl25grW1tUHBPcnkJDDJ0GxJ2UV310ZTQ6PR\n0Lpra47sPpKjaUOlAqKUDMSSUmJmZpYnbosIIcyEEC2BvsD7aD1dAAqnW4YCHD58OMvFsIKAu7s7\nb9Stm2IUw/61IOAfX0Jv7EDKf4H3SNTEc+D8AT5cuhT7EiVoXbcu7u7ueHl50VqnbTJe2/0INruc\nzm2wRrlytHJ0xMvLK9Vxc3NzfKZP5+bt2/QZNoz9jx6x9vx59j96RJ9hw7h5+zbe06alijI1NFtS\ndsnM4JjC4MbL24vgJ8FM8JmQo/ZKZWpSMkPU9l+3G3wOvQy6EOIP4DHaqZZ3gTPAe0BpKWUrg1Wo\nZMnTp09xc3Ojd+/ejBw5khcvXigtKde4+s8/DGyXmfNUabTJs1ZiJixYHxhIswkTaFOvHlf/+Ydj\nQUEMTOM1kuI++E56t0GAgW3bckzHO0UXa2trPD098du8mV/++AO/zZvx9PTMcERtaLYkYyClZNCg\nQXh7ext1f5zsoNFoWLFzhfZ+//JduvudFUplalIyQ1Ry34ai7wj9LNAfrQF3kVJ+IaXcJaWMzqqh\ninEoUaIEn376KUWKFGHZsmW0bNmSCxcuKC0rV9BoNOkCdFIjgI9oVqMvjapW5cq9e8z+6Seex8Tw\n8sWLXA3ueRWGBCUZi5CQEDZt2sT06dPp0KEDN27cMNq59cXL24vohtHa+90wOtujdKUCopQMxErp\n20D0DSxSDbjCmJmZ8fnnn3P06FHq1KnDuXPnaN68OevXr9f7HDExMaxZs4aBrq707N6dga6urFmz\nhpgYk3JawszMLHVwTyb1rK3KcHz2bEZ160aCRsPzly958Pgx1x88SKljaHBPdu6ZIUFJxqJp06bs\n37+fihUrcvjwYZo0acK2bduybmgkUkbnyfe7TvZG6UoFRCkZiJW2b0MovE7O+ZTmzZtz+vRpBg8e\nzMuXLylTJuv16MTERHymTqVqpUrsWLGCznZ2DGnUiM52duxYsYKqlSrhM3VqtsLKc5Na9evjFxiY\n8v7qg/sZ1rv24D5FLS351tOTxlWrYmFhQfSLF3j5+bEradsA3dE5kOEoPaPgnpzcM0OzJRmLjh07\nEhoaSs+ePXny5Anvv/9+nrk26o7OgWyP0pUKiFIyECtd3wagb05RFROiePHirF27lpEjR9K8efNX\n1tV1wUsOltFlcIcOWr/olSu5+M8/qVzwlGLTpk3UrV2b8IgIapQrxwt5BTObTumCe2LkQ0DrLRJ2\n9y6HDx9m7NixHDp0iO6zZ/N5jx7su30OW1EM8U/qtvvE3yltj4aFseXAgZTPc3rPXF1dGT92bIru\nw9cv4vysJiIydd+H4i7St5kL4RERHLt8mR9dXY17AwF7e3t+/vlnfH19WbduHe+++67R+8iIv47+\nhW2ULeJq6oCmfff36dX+8MnDOCc6I8JTtz904lCu7hapVL9p+w5A/5iEjFC3zy3gZCdIptvcubTv\n1StV9h6l0PVD18ef+1FiIpevXSMxMRHHOnW4Fh4OQLMaNfAfMwbHihUzbNve2xubSpVS+aEbcs8M\nzZaUGyQmJir+Ja2iH+p+6KgGPS3+/v60adMGOzu7PPWLNibJkaK1y5XD/xXRnq6LFnHtwYN0kaI1\nq1UjMjKS2IQEbKysWDZkCB+0b59yveEREbgtWsTD+PhUkaJpfcmTfeDT4lgxgZXD3kp3zwwJSlJR\nMdSgq1MuBYzg4GAGDRqEra0tbm5ur/SLfv1MrVTJGnT9oj09PfNaeiqKFSvGrbt3adygAa999ln6\n/VgCAzkSFkaVKlVSGXMAS0tLrt24wVudOnH46FGiY2MZ7OvLmgMH6N+6NduDgzkaFkar1q0J3L07\nVURuWl/yZB/49IwA0t8zc3NzNm3Zwoxp03h9ypRsZUvKS6Kiovi///s/pk6dSqdOnRTRoGJ8XpVT\n9JV5RNWcoqZJrVq16N69O48fP8bX15fnz58To+OSl9d+0YZQrFgxLl+7xqUrV4gtWZIvt2xh+Pff\n8+WWLcSWKsWlK1cIu3o1w82pLC0tCTh0iLv37vHGG28ghCDwn3/49IcfKFatGjfv3GH/gQPptlfI\nyJc8K9Les5wEJeU1S5YsISAggM6dOzNp0iTi4+MV06JiPF41Qh+ZZypUjIa9vT07d+5k6dKlfDpm\nDAcvXOD1iRPZPnYs9SpVytAvWneUXtrGhigTS2JdtWpVDh06lKO2ZcuW5dChQ4SFheHq6sqZM2fY\nvXs333//PePHj08Xlv7s6VNKV62arT4yu2fJQUlKP+1kxPjx44mLi2PGjBnMnj2b/fv34+/vT40a\nNZSWpmIAmY7QpZQ/6FvyUrBK1gghGDVqFN3eeosKpUpx9/FjbKysFPGLNhUcHR05evQon3/+OQkJ\nCUycOJEuXbrw77//pqqX1pdcH/LjPbOwsMDHx4eDBw9SpUoVgoODadKkCXfu3FFamooBqH7oBZi+\n/fvjVKsWeyZPpoq9vWJ+0aYS0GRlZcXXX3/NH3/8QdmyZdm/fz9OTk789ttvKXXS+pLrQ2b3zFSu\n+1W0bduWkJAQ+vTpQ79+/ahUqZLSklQMQN/tcy2BSYAb2gQXqfyxpJSKLtOrXi4ZY0iyBmN4uaTs\nWLhkCa0cHenr7Pzf4uDJkxwNC2PkqFFM8fbO8/nke/fu8cEHH7B3714ARo8ezdy5c9FoNKnuWXa9\nXEz9ujNDSkl8fHyh3rLZFMir/dDnot3L5SvgG2AyUB1wBaZIKVfkVIAxUA165mTmF63RaPghIICB\nbdtSxMLC6H7R+rrvea5ciYOjoyLuexqNhoULFzJx4kQSEhJwcnLC39+fLf7+OfYlzw/XrWK6GGrQ\n9Z1y6QcMTzLcicAvUsrRgDfafdFVTJQp3t6Uq1OHbnPnEh4RkXL8m99/x/O772jv40PQP//Qdc4c\nytetyxRvb6P0O2PaNCIuX2bXhAkZGjXQuvztmjCB+2FhzJg2zSj9ZgczMzPGjRvHkSNHqFWrFqGh\noTRv3pyKlStTtnbtdPdMl/CIiAzvWX647uwQEhJC7969uX8/4+0XVEwLfUfoMUA9KeVNIcRdoIeU\n8pQQogYQKqVUdEVIHaG/muQpgDlzt2JjWRv7EiWIS4jk9sPDJGhiADPe6/sum7dsMcpo0dDgHCWI\niorik08+YcOGDQC899571KpRg++//z7HCS7yw3VnRZs2bTh8+DAODg6sX7+eLkZeXzE1lExwAYaP\n0JFSZlmAi4BL0usg4Muk1+7AfX3OkZtFexkqWdGmzWQJUqc8lPCuRDuVLj09PWVMTIzB/axevVr2\naNFCyh9/lPLHH2X7+h+n6Vdb2tf/OKXOOy1ayNWrVxvhKg1jw4YNsnjx4hKQ1apVk/v375erV6+W\nA/r3lz27d5cD+veXq1evltHR0ena5ufrzoxbt27J9u3bp/yNeHl5ydjYWKVl5Rpbf9kqS7QrIbft\n3KZI/0m2LMe2UN8plx3Am0mvFwPThBDhwDrg+xx/m6jkKelH32WA7dSp8w5Fixbln3/+wcLC8OBh\nYwTnKMXAgQM5c+YMr7/+Ojdu3KBLly7cuXOHHzZuzFGCi6wwlevOjMqVK7N//35mzJiBubk58+fP\np1u3biaRFcnYSAUTXBgLffdDnyilnJX0ehvQBlgC9JFSTspFfSq5jqBiRWdOnDjBpk2bKPKKRUB9\nySjRQ1YYO9GDIdSuXZtDhw4xfvx4EhMTmTp1Km+++Sa3b99+Zbv8ft2ZYW5uzuTJkwkMDKRatWoM\nGTLEZHOZGoKSCS6Mhb4p6NoJIVKGblLKYCnlQmCXECKzXGEq+YiGDRtSvXp1o5yrIATnWFpaMnfu\nXPbs2UP58uUJCAjAycmJn3/+OdM2BeG6X0Xr1q25cOECAwYMUFqK0UkenSuR4MKY6DvlcgDt83la\nSiZ9plJAefDgAfv3789Wm7TBOYmZ/FPoHs+NgCaAyMhIPDw8qF+7NjUrV6Z+7dp4eHgQGRmZdWPg\njTfeYMKECVSsUIFHjx7x7rvv0rFjRx4+fJiurildd25hqou3hqJkggtjou+EqSDjTGB2gJqWLp/g\n6FgU8MnkeHqklHh4ePDHH38wfvx4ZsyYodeUTHKihyv37uEXGMiJK2cpY9ODsra2WJibk5CYyINn\nzzhx5TI+P0YysF07oyd6iIuLo3uXLhw9epTWdesyoWvX/3ZqDAqiaqVKtGrVij/37MkwmCZtcNCM\n3r05fPEiPwQEcPDgQRzKlWPoRx+xZNmylLUJU7hupVi5ciVPnjxh3Lhx6fbHyQ8omeDCmLzSbVEI\nsTPp5TvAPkA3k6450BD4R0rZLdcU6oHqtpg7JCYmMnv2bHx8fNBoNLz++uv4+/tTq1atLNtOnTyZ\ntcuXU9vBgTUjRmQeYOPry5X79/EYPpzpM2caRXdcXByvOTpS1tKSTa/YS9198WIi4+JS7YcOrw4O\nOhMejuuiRYTdvYuZEDRt0oRjx4+nLCYred1Kcf/+fWrUqMGLFy/o3Lkz69evp0KFCkrLypfkdmDR\nw6QigMc67x8Ct4HlwMDsdCiE+EQIcUII8VIIseYV9T4UQiQkbdGbvJWvOl+fh5ibmzNlyhQCAwOp\nWrUqJ06coGnTpmzcuFGv9lXs7dk1adKrA2wmTaKKvb0xZdO9SxfKWlpy0MfnlX0f9PHB3tKS7mmm\nPF4VHNS0Rg1OzZ2LZ8eOaKTk1JkzNGrYkEePHqXUUeq6lcLBwYGtW7dib2/Pvn37aNy4MX/88YfS\nsgol+gYWeQMLpJQGT68IIXoDGqArUExKmeHeokKID4EhUsosjXhej9CHDp1DWNjLdMcdHYuycuUX\neaYju5iZtUbK9JsvCXEHjebIK9t++KE3f/65jQcPLlClSmtq1tQGCGd0zUoG2ERGRlK1UiUuLFxI\n9XLlqPfpWu49LpWuXvnST7i4yIPwiAgafP45N+/cwd7ePp32ZGQGWZ62HDnC/5Yv5/nLl1SqVInV\nq1czwNU1X2aIMgZ3795l0KBBKWsuixYtYsyYMQqryl/kScYiKeW0pM6cgVrAb1LKaCGEDRArpUzQ\nt0Mp5c9J53odyJdbu4WFvSQgwCeDTzI6ZjpojfnWDI6/n2XbGzcEDx78DWzn1q1e3LqVPJfuk66u\noVl/DMHLy4vWdeum9H3vcSmevsgo4717St+tHB3x8vJi7dq16bQnk1GWp/6tW9Oidm2aTZjAnTt3\n6N69O7UrVKCynV2WbY193aZAhQoV2LNnD/Pnz2fOnDl0795daUmFDn3dFh2EEMHAcWAT4JD00ULg\n61zSBtBUCBEhhLgohJgshMh/qy0FCgG8R5rNNtOhZIDNsaAgBrZtm602A9u25VhQEJCxdvmKLE81\nypVj3oABNHjtNaSUXP73Xzr4+HDjwYMs24LpBxZlFzMzMyZMmEB4eDiOjo5Kyyl06Ovl8g1wD61X\ny02d41vRBhjlBgFAQynlDSFEA+BHIB6Ym1FlHx+flNcdOnSgQ4cOuSRLJSNOnz5NxYoVKV++vFGz\n/mSX2JcvcxTcE5eUpi8j7VlleSpra0utatWwtbHh0j//cPjSJZy8vFg1bBjCUuS7DFHGoFSp9NNc\nKuk5ePAgBw8eNNr59DXobwJvSikfp4kQu4p2f3SjI6W8rvP6vBBiOjAOPQy6St7y6NEjevXqRVxc\nHOvWrVM0wMaqaNEc9W1pZQWkDw5KHmHHtNPJ8hS4kz5NW6bMhydrL2Fri1vDhuw7d46dJ0/S75tv\nKFvBlpjBWbctDEgpGTt2LO+//z6tWrVSWo5JkHbwOc3A3Tf1ncIoBsRlcLwskH51MPcoePHGBYD4\n+HgcHR2JiIjg7bff5sHjx/x4/Hi2zvGqAJvsZP5xadsWv6TpE33xCwrCJWmaJm1wUHayPHXq0oU9\n58/zs5cXSz09KWJuzoO7z2AV2ufbPMoQZaps27aNb775hrZt2zJ79mwSExOVllTg0HeEHggMBr5M\nei+FEObABCBbYYRJ7Yqg9WO3EEJYAQlSysQ09boBp6WUEUKIemiTamzJTl+5RXYDdEwFIe5kuAAq\nRNZ5JF91zQ4ODimLYZMnT2bPnj2Ym5uz/9w53mzUCMeKCSQvgKZqW1G7lh4eEZFhgE2GmX+qVtVu\nYbtiBePHjk2X+Wf+/PlUrVSJ8IgIapQrR/nST0heANVFe1zb99GwMLYc0AY8JwcHJbc/fP0izs9q\nInQCSyVwKO4ifZu5pNM+fuxYrj94wCfdunH0Thg7Dh0nJjIOsUJQq145KlQrnWnbgk6vXr0YN24c\nCxYsYNKkSezbt48NGzaoae+MiL5ui6+hndMOAdoDvwEN0Ib+vyGlvKp3h1oXSG9SR55OA9YCF4D6\nUsrbQoj5wCDABrgPbABmpjX8SedUA4tMhGPHjuHu7k54eDiNqlfnxKxZ2c76A4Zl/nmzQwdi/v2X\ngz4+Wfbd3tsbm0qV2K8zj5lZlid9tKdtGxMby2c//MDKffsA+L/mzVnz8ceUKFbMqBmi8hO7d+/m\nww8/5P79+9jZ2bFv3z6aNGmitCyTIE9S0CV1VAH4GGiGdqrmNLBMSnk3p50bC9WgmxZPnz5l586d\n/PbLL1kaZI8VKyhft266VGzZMard5s6lfa9eKYZR30hRt0WLeBgfn61I0ay0Z9Z2+7Fj/G/FCp5E\nR1PO1pbydnbUb9680Kagu3//PoMHD+bOnTscP36cokVN++k2r8gzg27KFBaDXq+eK/fupf/DL1/+\nJRcvbs7VvnMSTJU8ZTJj1m8IalDE3BwzIdBISXxiIpJwpk7+v1RZf8A4gUm6e7m0cnRkYNu2qfZy\nORoWRqvWrflz9+5X7+WydKneGYuyanvl3j1m//wzj58/B2DChAl6749TENFoNDx8+JCyZcum+0wq\nnDlIKXI1YxFgDSwD7gARaH3Q7Q3JqJEbhUKSsahkyQ8zzIBTsuSHud53+/beGWffae+dZVtb2w90\n2hySkCBBezwjjJn558GDB3Lw4MGyXq1asmblyrJerVpy8ODB8sGDB3pdd3R0tN4Zi/Rpu3LlSjlp\n0iRpZmYmAdmyZUt59epVvbQUJpTOHKQUGJixKKtF0WloF0M3ovVmcQO+A7IOLVRRSeK/EdYRtEsw\n7YENmY68chqYtH/PnnQRl/b29qxduzbbmpOxtrbG09MzR5Gcr2rbtWtXBgwYQHBwME2aNGHFihW4\nubnlWGdB4unTp4wcO5KoPtrMQX169ClUo3RDyMptsQ/a/VSGSilHo911sXeSp4qKSjZ5iTY27S+g\nMfHxtzKsVVAz/+jStm1bQkJC6NOnD1FRUbi7u+Ph4cHzpOmYwkzffn25f+U+LIeQ6JB8tye5kmRl\n0KugTQoNgJTyOJAAVMxNUSoFlU5AKNAFeEhMzF+MHj2aly9Tz80X9Mw/yZQpU4Zt27axfPlyihYt\nyrp162jWrBmnT59WWppiSCmJNIvU7vL0DGJ3xTLmizHEx8crLS1fkJVBNyd9QFEC+vuvq6ikoTzw\nJzAfEGzcuDFd9p+0wT36kF8DdIQQDBs2jJMnT9KoUSMuX76Mi4sLCxcuRKPRKC0vz9n+63YuV7gM\nnmgzF0u4c+EOTk2c0n3xq6QnK8MsAD8hhG5ii6LAKiFESpielLJnbohTSU358i/RLmlkdDx3MSSY\nKjPdpUp1ZuXKcekCS9IG9+Q0MCk/0aBBA4KDg/Hy8mLZsmWMHTuWvXv3sm7dOhwcHLI+QQEhJXPQ\nTQG14LH1Yy4GXcSiqIXq2qgHWWUs0ms1SUrpYTRFOaCwuC0WJgwJ7snv/PLLL3h6evLo0SMcHBxY\nv349XfLh04exePDgATY2NgViz/isUP3QUQ16QUOj0XDmzBnmffVVjgOT8ju3b99m0KBBKTvxeXl5\nMXPmzAx95lUKDqpBR2vQzc37A1C8+EOePNn7yvqGBugY0t6QtoZmSjKkfV5maZo7dy5ffvklU6ZM\nQZOQgK+vb7aDewoCiYmJzJkzB29vbxITE3F2dmbTpk3UqVNHaWkmwenTpzl69CgjRowoMG6NuRpY\nlF8KkBJsYm7eP0vnfUMDdAxpb0hbQ4J7DG1vaN/ZYdKkSVL7O0W2a9dOhoWF5Ti4pyBw5MgRWa1a\nNQnI4sWLy/Xr1ystSXFevnwp69SpIwHZs2dPGRkZqbQko4CBgUVqBiAVk2PmzJns3buX8uXLExgY\niIuLC2XKlMFv82Z++eMP/JJSthWGOVWAVq1aERISQv/+/Xn+/DkffPABAwcO5Fk+8rs3NlZWVsya\nNYuSJUuyc+dOnJycjJooIr+iGnQVk6Rz586cPXuWt99+m0ePHvH1118XSje+ZEqVKoW/vz+rV6/G\n2tqajRs30rRpU45nc9/5gsT7779PaGgorVu35s6dO3Tq1ImvvvpKaVmKohp0FZOlbNmy/PbbbyxZ\nsgQ/Pz/MzAr3n6sQAk9PT06dOkWTJk24du0ab7zxBvPmzSu0X3bVqlUjICCAqVOnIoSgZs2aSktS\nlML9H6Ji8gghGDlyJNWqVVNaislQr149jh07xqeffkpCQgITJkyga9eu3L2r+E7WimBhYcG0adO4\ncOEC/fv3V1qOohSYiE9zc21QSfHiD7OoaXiAjiHtDWlraKYkQ9qbYpamyMhIhBDY2dkppkEprKys\n+Oabb+jcuTODBw9m3759NG7cmB9++IG3335baXmKULduXaUlKE6BcVssCNehoj8ajYYePXpw9uxZ\nNm7cSPv27ZWWpBh3797lgw8+YF9SVqQxY8Ywd+5crJISXxd2Nm3aRP369WnatKnSUrLEULdFdcpF\nJV/y7Nkznjx5wp07d+jYsSNTp04lISFBaVmKUKFCBXbv3s3cuXOxsLBg8eLFuLi4cOnSJaWlKc7f\nf/+Np6cnLi4uLF68mII+8CswI/T27b2B3Al0SUteBtmYQr+Gklu6ExISmDZtGrNmzUJKSevWrdm0\naVOhnm8/fvw4bm5uXLt2DWtra5YsWYKHh0eBCbzJLi9evGDcuHH4+voC8M4777B27doMsySZAmpg\nUZ6rb5AAABH0SURBVJrAotwIdElLXgbZmEK/hpLbug8cOCArVqwoATl16lSjnDM/8/TpUzlgwICU\n4Kz+/fvLx48fKy1LUXbs2CFLly4tAVm+fHl5+PBhpSVlCGpgkUphp0OHDpw9e5YvvviCyZMnKy1H\ncWxtbfHz82P9+vUUL16cLVu20KRJE44cOaK0NMXo3bs3oaGhtGvXjufPn1Mug72BCgKqQVcpENjZ\n2fHVV18V2oTLGTFo0CBOnz5N8+bNuXHjBu3atWPWrFkkJiYqLU0RqlSpwl9//cWhQ4eoXbu20nJy\nBdWgqxR4bt26VeAXwzKjTp06HDlyhHHjxpGYmMjkyZPp3Lkzd+7cUVqaIpibm+Pk5KS0jFxDNegq\nBZrIyEhcXFzo2bMnDx48UFqOIlhaWjJ//nx27dqFg4MDBw8epHHjxuzcuVNpaSaDlJKlS5cSFRWl\ntBSDKDCBRe3b+wB5E+iiVJCNKQb36IOSui9evEhMTAy//fYbTk5ObNiwgTfffDPX+zVFunbtSmho\nKIMHD2bXrl306tWLTz75hPnz51OsWDGl5SnKqlWrGDVqFIsXL8bf3x9nZ2elJeUMQ1ZUTaVoL0NF\nJWNu3Lgh27ZtKwEphJBffPGFjIuLU1qWYiQmJsqvv/5aFilSRAKyUaNG8vz580rLUpQLFy7Ixo0b\nS0BaWFjIefPmycTExDzXgYFeLoobY2MU1aCrZEV8fLz08fGRZmZmEpCBgYFKS1KcU6dOpewpXrRo\nUbl8+XKp0WiUlqUYL168kKNHj05x93zrrbfyfJ91Qw16gQksKgjXoZL7HDp0iCNHjjB+/HilpZgE\nz58/Z9SoUaxbtw6APn36sGrVKsqUKaOsMAX59ddf8fDwoEqVKhw7dixPt1BQU9ChGnQVFUPZtGkT\nw4cPJyoqiipVqrBx40batm2rtCzFuHPnDi9evMhz90bVoKMadBXjEBERUWADTvTh2rVruLu7Exwc\njJmZGVOmTGHy5MlYWBQY3wmTR92cS0XFCAQFBVGtWrVCnRmpZs2aBAUFMXHiRKSUTJs2jY4dO3Lz\n5k2lpZkMT548YdOmTZjqAFI16CoqQEBAAC9fvmTcuHG888473L9/X2lJilCkSBFmz57Nvn37qFCh\nAocOHcLJyYnt27crLU1xpJQMHz6cAQMGMGDAAJ4+faq0pPQYsqJqKgXVy0XFCOzcuVPa2dlJQDo4\nOMjdu3crLUlRHjx4IHv06JHi9TF06FAZHR2ttCzF0Gg0cu3atdLGxkYCskaNGvLYsWNG7QPVbVE1\n6CrG4/bt27JDhw4SkGXLlpVRUVFKS1IUjUYjv/32W2lpaSkB+dprr8nQ0FClZSnKpUuXZNOmTVN8\n1r/66iujuXsaatDzfMpFCPGJEOKEEOKlEGJNFnU/E0LcFUI8FkJ8L4RQd15SyVUqVarEvn37mDVr\nFmvWrKF48eJKS1IUIQSjRo3i+PHj1KtXjwsXLtCiRQuWLVuWPJgqdDg6OnL06FE+//xzEhISuHXr\nlsnsN5/nXi5CiN6ABugKFJNSemZSryuwDugI3AV+Bo5KKb/MoK4srH9cKip5RXR0NJ999hmrVq0C\noGfPnqxevRp7e3uFlSnHX3/9RatWrYy2dUK+dVsUQswAKr3CoG8EwqWUk5Ped+L/27v/6KjKO4/j\n748JEFrIGlntShWI1Z6N7hI2EpElC0d0qbKuFYFDxFpEpehyDrKyRpBdfogRka2VbRFFENcTRWKU\n3eKPLbTZFBHtWjvG8utQxcihGI1CgEAMJjz7x72JYzqTTEjInR/f1zn3kHvvM3e+9+E535l55pnn\ngWedc+dGKNuhFYsSdeUfE7ympiaOHz9O3759gw4lMGVlZUybNo3a2lr69+9PSUkJV1xxRdBhJYWE\nXbEIWAw81cb5d4GJYfv9gCYgK0LZDq2Ck6gr/5jgPfjgg+6CCy7o8i/DEk1VVZUbMWJEy/w48+bN\nS+n5cVoLhUJu586dHX4cidaH3gF9gPBxQYcBAan71sgEqrGxkQ0bNrB3714KCgp46KGHUnbM+sCB\nA6moqGD+/PlIori4mFGjRlFVVRV0aIE7evQoEyZM4NJLL2X16tXd+l1DPCf0OiAzbD8Tb/hUlAmL\nFwILqaqqoKKi4jSHZlJReno6r7/+OrNmzaKxsZG5c+cyZswYDhw4EHRogUhPT2fRokWUl5dz3nnn\n8eabb5Kbm8v69euDDi1QkigoKKC+vp5p06YxadIkamtrI5atqKhg4cKFLVundebtfWc22u9yeRZY\nHLY/GjgQpax1uZhu9corr7izzz7bAe6GG24IOpzAff75527cuHEtY9ZvvfVWV1dXF3RYgSopKXF9\n+/Z1gBswYIDbunVru48h0bpcJKVJygDSgHRJvSSlRSj6DHCbpBxJWcA8YG13xmpMNGPHjqWyspKJ\nEyfy6KOPBh1O4M466yxefPFFVq5cSUZGBk899RR5eXmEQqGgQwvMTTfdRCgUIj8/n3379nXLJ7kg\nhi0uABbgvZI3W4SXrHcCOc65/X7ZWcAcIAMoA+50zn0Z4Zo2ysWYOLF9+3YKCwvZsWMHPXv2ZOnS\npdx1111xM1a7u504cYKNGzcyfvz4dssm7LDFrmTj0E28OX78OL17907ZJFZfX8/s2bNZuXIl4H2i\nWbt2bUrPZhkLm23RmDhz8uRJxo0bR2FhYdQvw5Jd7969eeyxx9iwYQNZWVm8+uqr5Obmsnnz5qBD\niyvl5eXU19d32fUsoRvTxXbt2sW2bdsoLS1lyJAhbNu2LeiQAnP99ddTWVnJyJEjqa6uZsyYMdx7\n772cOHEi6NACFwqFuOaaa8jPz2f79u1dck1L6MZ0sUsuuYRQKMTQoUP56KOPGDlyJA888ABNTU1B\nhxaI888/n/LychYvXkxaWhoPP/wwBQUFfPDBB0GHFqi0tDSys7PZsWMH+fn5Ld1TndKZITLxsmGz\nLZo41NDQ4IqKilqG8q1atSrokAK3detWN2DAAAe4Pn36uJKSkqBDClRdXZ277bbbWtoItki0fSlq\n4tumTZt4/PHHKS0tteXcgEOHDjF9+nReeOEFAG6++WZWrFiR0vPjrF+/nrKyMsrKymyUiyV0YxKL\nc441a9Ywc+bMlsWY161bx9ChQ4MOLVA2ysWYBJaqXw5K4vbbb+edd94hNzeX999/n+HDh7Ns2bKU\nnR+nK1hCNyYgNTU15OTk8MQTT5CqnzBzcnJ46623mDlzJo2NjRQVFXH11VdTXV0ddGgJyRK6MQEp\nLS1l79693HHHHUyYMIGDBw8GHVIgMjIyWL58ORs3bqRfv35s3ryZwYMH89prrwUdWsKxhJ6ibEbK\njuvqOpsxYwbr1q0jMzOTl156idzcXLZs2dKlzxGkjtbXtddey3vvvcfo0aOpqalh7NixzJ49m4aG\nhtMTYBKyhJ6iLKF33Omos8LCQt59910uv/xy9u/fz5VXXpk0c4qfSn3179+fTZs2sWTJEtLS0njk\nkUcYPnw4e/bs6foAk5AldGMClp2dzZYtW7jvvvu45557GDRoUNAhBSotLY05c+bwxhtvkJ2dTSgU\nIi8vj6effjplv2uIlSV0Y+JAjx49KC4upri4OOhQ4sawYcMIhULceOONHDt2jKlTpzJ58mQOHz7c\n/oNTVNKMQw86BmOM6Qop/8MiY4wx1uVijDFJwxK6McYkCUvoxhiTJCyhG2NMkkiohC7pIkn1kp5p\no8xSSZ9JqpG0tDvji0ft1ZmkBZJOSDoi6aj/76DujTI+SKrw66q5Lna1UdbaGbHXmbWzr0gqlLRT\nUp2kP0gaEaXcP0v6WNIhSasl9Wjv2gmV0IGfAf8X7aSk6cB1wF8Dg4FrJf2om2KLV23Wme9551ym\nc66v/29VN8QVjxzwT2F1kROpkLWzr4mpznwp384k/T2wBJjinOsDjAT2Rij3PaAIuAIYBHwHWNTe\n9RMmoUsqBA4Bv2qj2A+BHzvnPnbOfQz8GLilG8KLSzHWmfm6WMYAWzv7ulMeN52CFgL3O+feBghr\nQ639EFjjnNvtnDsMLAamtnfxhEjokjLxXp1m03bjuQSoDNuv9I+lnA7UGcA/+t0Hv5d0x+mPLq4t\nkfSppNcljYpSxtrZ18VSZ5Di7UzSGcBQ4By/q2WfpJ9K6hWheKQ2do6krLaeIyESOnA/8KRz7o/t\nlOsDhP8u+LB/LBXFWmfrgRzgbOBHwHxJk053cHGqCLgA+DbwJLBRUnaEctbOvhJrnVk7g28BPYDx\nwAhgCPA3wL9GKBupjQloc52+uE/okoYAVwGPxlC8DsgM28/0j6WUjtSZ/5Gu2l+z9k1gOTDhdMcY\nj5xzbzvnjjnnvnTOPQO8AYyNUNTamS/WOrN2BkC9/+9/OOc+dc4dBB4h9jbmgKNtPUEirFg7ChgI\n7JMkvFeuNEkXO+daL0C4A8gFfuvvD/GPpZqO1FlrDusTbRatLqydRRdr+0m5duacq5W0P8bizW2s\nzN8fAnzinDvU3pPE9QZkAOeEbcuAUuCsCGWn+xXR39+2A9OCvoc4r7PrgDP9vy8D9gM/CPoeAqiz\nPwPGAL2ANOAmvHdDF0Uoa+2s43Vm7cy790XAb/C6nrKALcDCCOW+BxzA66bKwhvYUNzu9YO+wVOo\nkAXAM/7fBcCRVucfAj4HPgOWBB1vPGxt1RnwnF9XR4CdwIyg4w2ojv4cb3jnYeAgsA0YHanO/GMp\n3846UmfWzlrqIR1YgTf67ADwE6AncL5fN+eFlZ0FVAO1wGqgR3vXt9kWjTEmScT9l6LGGGNiYwnd\nGGOShCV0Y4xJEpbQjTEmSVhCN8aYJGEJ3RhjkoQldGOMSRKW0E1KkzRFUpvzY0j6UNLd3RVTWyQN\nlHRSUl7QsZj4YwndBE7SWj9JNfmr2nwgaZmkb3TwGj8/xRDi8td1bdxTXMZrgpcIk3OZ1LAZ+AHe\nz6D/DlgDfAOYEWRQcSqlJrUysbN36CZeNDjnapxzf3TOPQ88C1zffFLSxZJe9tei/ETSc5K+5Z9b\nAEwB/iHsnf5I/9wSSbslHfe7TpZK6tmZQCVlSlrlx3FE0v9KujTs/BR/3czR/mIOdZLKJQ1sdZ25\nkqr9azwtab6kD9u7J98gSZskHZO0Q9JVnbknkxwsoZt49QXeYgBIOhf4NfAe3oovVwLfBJq7I/4d\nbzbJX+ItInAu3kRR4M0rfQvwl8CdwCRgXidjexX4C7x5rIfgzZj3q+YXGF8vYI7/3JcDZwKPN5/0\nlwecD8wF8oDdwN181Z3S1j0BPIA33/1g4G1gXUe6qEySCnr2MdtsA9YCPw/bvwyoAZ7z9+8HNrd6\nTBZwEhga6RptPNd0YE/Y/hRazaQY4TEfAnf7f4/GmxWvV6syIeBfwq7ZBFwYdn4y8EXY/jZgRatr\n/ALYG61e/GMD/fu+PexYf//Y3wb9f2lbsJv1oZt4cY0/2iTd3/4LmOmfywNGRRiN4vBWQ/8tUUia\nANwFXIi/0Aed+2Sah/fp4DNv7ZAWvfxYmjU4594P2z8A9JB0pnOuFu8Tw6pW1/4NcFGMcfy++Q/n\n3AE/lnNifKxJUpbQTbz4NTANaAQOOOeaws6dAbxM5AWvP4l2QUnDgHV488H/Am9e6e/jLfhxqs7A\nm6O6IEIsR8L+bmx1rrkr5YwIx07Fl1FiMynMErqJF8edcx9GOfc7YCKwr1WiD3cC7913uBHAfufc\ng80HJA3qZJy/w+vTdm3EG4vdeF1L/xl2bFirMpHuyZio7BXdJIIVeMudlUq6TFK2pKskPSHpm36Z\nKuCvJH1XUj9J6cAe4NuSJvuPuRMo7Ewgzrlf4i2E/N+SrpY0SNJwSQsljWjn4eHv6JcDt0iaKulC\nSUV4CT78XXukezImKkvoJu455z7Ge7fdBLyGt4bnT/FGwjT4xZ4EduH1p3+K9wXhy3jdKz8BKvFG\nx/zbqYTQan8sUI7XB74beB74Ll4/eUzXcc6tBxYDS/De9V+MNwrmi7Dyf3JPUeKJdsykGFuCzpg4\nIeklIM059/2gYzGJyT7CGRMASb3xxsX/D94nj/HAdcANQcZlEpu9QzcmAJIygI14P0zqDfwBWOq8\nX8kac0osoRtjTJKwL0WNMSZJWEI3xpgkYQndGGOShCV0Y4xJEpbQjTEmSfw/vQFmOIgED+4AAAAA\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f112d76e390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.linear_model import SGDClassifier\n",
    "\n",
    "sgd_clf = SGDClassifier(loss=\"hinge\", alpha = 0.017, n_iter = 50, random_state=42)\n",
    "sgd_clf.fit(X, y.ravel())\n",
    "\n",
    "m = len(X)\n",
    "t = y * 2 - 1  # -1 if t==0, +1 if t==1\n",
    "X_b = np.c_[np.ones((m, 1)), X]  # Add bias input x0=1\n",
    "X_b_t = X_b * t\n",
    "sgd_theta = np.r_[sgd_clf.intercept_[0], sgd_clf.coef_[0]]\n",
    "print(sgd_theta)\n",
    "support_vectors_idx = (X_b_t.dot(sgd_theta) < 1).ravel()\n",
    "sgd_clf.support_vectors_ = X[support_vectors_idx]\n",
    "sgd_clf.C = C\n",
    "\n",
    "plt.figure(figsize=(5.5,3.2))\n",
    "plt.plot(X[:, 0][yr==1], X[:, 1][yr==1], \"g^\")\n",
    "plt.plot(X[:, 0][yr==0], X[:, 1][yr==0], \"bs\")\n",
    "plot_svc_decision_boundary(sgd_clf, 4, 6)\n",
    "plt.xlabel(\"Petal length\", fontsize=14)\n",
    "plt.ylabel(\"Petal width\", fontsize=14)\n",
    "plt.title(\"SGDClassifier\", fontsize=14)\n",
    "plt.axis([4, 6, 0.8, 2.8])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Exercise solutions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## 1. to 7."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "source": [
    "See appendix A."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 8."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_Exercise: train a `LinearSVC` on a linearly separable dataset. Then train an `SVC` and a `SGDClassifier` on the same dataset. See if you can get them to produce roughly the same model._"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's use the Iris dataset: the Iris Setosa and Iris Versicolor classes are linearly separable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn import datasets\n",
    "\n",
    "iris = datasets.load_iris()\n",
    "X = iris[\"data\"][:, (2, 3)]  # petal length, petal width\n",
    "y = iris[\"target\"]\n",
    "\n",
    "setosa_or_versicolor = (y == 0) | (y == 1)\n",
    "X = X[setosa_or_versicolor]\n",
    "y = y[setosa_or_versicolor]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LinearSVC:                    [ 0.28480873] [[ 1.0554257   1.09851869]]\n",
      "SVC:                          [ 0.31933577] [[ 1.1223101   1.02531081]]\n",
      "SGDClassifier(alpha=0.00200): [ 0.32] [[ 1.12293103  1.02620763]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import SVC, LinearSVC\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "C = 5\n",
    "alpha = 1 / (C * len(X))\n",
    "\n",
    "lin_clf = LinearSVC(loss=\"hinge\", C=C)\n",
    "svm_clf = SVC(kernel=\"linear\", C=C)\n",
    "sgd_clf = SGDClassifier(loss=\"hinge\", learning_rate=\"constant\", eta0=0.001, alpha=alpha,\n",
    "                        n_iter=100000, random_state=42)\n",
    "\n",
    "scaler = StandardScaler()\n",
    "X_scaled = scaler.fit_transform(X)\n",
    "\n",
    "lin_clf.fit(X_scaled, y)\n",
    "svm_clf.fit(X_scaled, y)\n",
    "sgd_clf.fit(X_scaled, y)\n",
    "\n",
    "print(\"LinearSVC:                   \", lin_clf.intercept_, lin_clf.coef_)\n",
    "print(\"SVC:                         \", svm_clf.intercept_, svm_clf.coef_)\n",
    "print(\"SGDClassifier(alpha={:.5f}):\".format(sgd_clf.alpha), sgd_clf.intercept_, sgd_clf.coef_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's plot the decision boundaries of these three models:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAp4AAAEYCAYAAAD1d/bLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f112d82e9b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compute the slope and bias of each decision boundary\n",
    "w1 = -lin_clf.coef_[0, 0]/lin_clf.coef_[0, 1]\n",
    "b1 = -lin_clf.intercept_[0]/lin_clf.coef_[0, 1]\n",
    "w2 = -svm_clf.coef_[0, 0]/svm_clf.coef_[0, 1]\n",
    "b2 = -svm_clf.intercept_[0]/svm_clf.coef_[0, 1]\n",
    "w3 = -sgd_clf.coef_[0, 0]/sgd_clf.coef_[0, 1]\n",
    "b3 = -sgd_clf.intercept_[0]/sgd_clf.coef_[0, 1]\n",
    "\n",
    "# Transform the decision boundary lines back to the original scale\n",
    "line1 = scaler.inverse_transform([[-10, -10 * w1 + b1], [10, 10 * w1 + b1]])\n",
    "line2 = scaler.inverse_transform([[-10, -10 * w2 + b2], [10, 10 * w2 + b2]])\n",
    "line3 = scaler.inverse_transform([[-10, -10 * w3 + b3], [10, 10 * w3 + b3]])\n",
    "\n",
    "# Plot all three decision boundaries\n",
    "plt.figure(figsize=(11, 4))\n",
    "plt.plot(line1[:, 0], line1[:, 1], \"k:\", label=\"LinearSVC\")\n",
    "plt.plot(line2[:, 0], line2[:, 1], \"b--\", linewidth=2, label=\"SVC\")\n",
    "plt.plot(line3[:, 0], line3[:, 1], \"r-\", label=\"SGDClassifier\")\n",
    "plt.plot(X[:, 0][y==1], X[:, 1][y==1], \"bs\") # label=\"Iris-Versicolor\"\n",
    "plt.plot(X[:, 0][y==0], X[:, 1][y==0], \"yo\") # label=\"Iris-Setosa\"\n",
    "plt.xlabel(\"Petal length\", fontsize=14)\n",
    "plt.ylabel(\"Petal width\", fontsize=14)\n",
    "plt.legend(loc=\"upper center\", fontsize=14)\n",
    "plt.axis([0, 5.5, 0, 2])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Close enough!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 9."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_Exercise: train an SVM classifier on the MNIST dataset. Since SVM classifiers are binary classifiers, you will need to use one-versus-all to classify all 10 digits. You may want to tune the hyperparameters using small validation sets to speed up the process. What accuracy can you reach?_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, let's load the dataset and split it into a training set and a test set. We could use `train_test_split()` but people usually just take the first 60,000 instances for the training set, and the last 10,000 instances for the test set (this makes it possible to compare your model's performance with others): "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.datasets import fetch_mldata\n",
    "\n",
    "mnist = fetch_mldata(\"MNIST original\")\n",
    "X = mnist[\"data\"]\n",
    "y = mnist[\"target\"]\n",
    "\n",
    "X_train = X[:60000]\n",
    "y_train = y[:60000]\n",
    "X_test = X[60000:]\n",
    "y_test = y[60000:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Many training algorithms are sensitive to the order of the training instances, so it's generally good practice to shuffle them first:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np.random.seed(42)\n",
    "rnd_idx = np.random.permutation(60000)\n",
    "X_train = X_train[rnd_idx]\n",
    "y_train = y_train[rnd_idx]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's start simple, with a linear SVM classifier. It will automatically use the One-vs-All (also called One-vs-the-Rest, OvR) strategy, so there's nothing special we need to do. Easy!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearSVC(C=1.0, class_weight=None, dual=True, fit_intercept=True,\n",
       "     intercept_scaling=1, loss='squared_hinge', max_iter=1000,\n",
       "     multi_class='ovr', penalty='l2', random_state=None, tol=0.0001,\n",
       "     verbose=0)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lin_clf = LinearSVC()\n",
    "lin_clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's make predictions on the training set and measure the accuracy (we don't want to measure it on the test set yet, since we have not selected and trained the final model yet):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.85318333333333329"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "y_pred = lin_clf.predict(X_train)\n",
    "accuracy_score(y_train, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Wow, 85% accuracy on MNIST is a really bad performance. This linear model is certainly too simple for MNIST, but perhaps we just needed to scale the data first:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "scaler = StandardScaler()\n",
    "X_train_scaled = scaler.fit_transform(X_train.astype(np.float32))\n",
    "X_test_scaled = scaler.transform(X_test.astype(np.float32))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearSVC(C=1.0, class_weight=None, dual=True, fit_intercept=True,\n",
       "     intercept_scaling=1, loss='squared_hinge', max_iter=1000,\n",
       "     multi_class='ovr', penalty='l2', random_state=None, tol=0.0001,\n",
       "     verbose=0)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lin_clf = LinearSVC()\n",
    "lin_clf.fit(X_train_scaled, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.92130000000000001"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = lin_clf.predict(X_train_scaled)\n",
    "accuracy_score(y_train, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That's much better (we almost divided the error rate by half), but still not great at all for MNIST. If we want to use an SVM, we will have to use a kernel. Let's try an `SVC` with an RBF kernel (the default).\n",
    "\n",
    "**Warning**: if you are using Scikit-Learn ≤ 0.19, the `SVC` class will use the One-vs-One (OvO) strategy by default, so you must explicitly set `decision_function_shape=\"ovr\"` if you want to use the OvR strategy instead (OvR is the default since 0.19)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma='auto', kernel='rbf',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm_clf = SVC(decision_function_shape=\"ovr\")\n",
    "svm_clf.fit(X_train_scaled[:10000], y_train[:10000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.94615000000000005"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = svm_clf.predict(X_train_scaled)\n",
    "accuracy_score(y_train, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That's promising, we get better performance even though we trained the model on 6 times less data. Let's tune the hyperparameters by doing a randomized search with cross validation. We will do this on a small dataset just to speed up the process:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 10 candidates, totalling 30 fits\n",
      "[CV] gamma=0.00177075633072, C=22.3504528445 .........................\n",
      "[CV] .......... gamma=0.00177075633072, C=22.3504528445, total=   1.3s\n",
      "[CV] gamma=0.00177075633072, C=22.3504528445 .........................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    2.0s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] .......... gamma=0.00177075633072, C=22.3504528445, total=   1.2s\n",
      "[CV] gamma=0.00177075633072, C=22.3504528445 .........................\n",
      "[CV] .......... gamma=0.00177075633072, C=22.3504528445, total=   1.1s\n",
      "[CV] gamma=0.187983142488, C=23.3461384887 ...........................\n",
      "[CV] ............ gamma=0.187983142488, C=23.3461384887, total=   1.4s\n",
      "[CV] gamma=0.187983142488, C=23.3461384887 ...........................\n",
      "[CV] ............ gamma=0.187983142488, C=23.3461384887, total=   1.3s\n",
      "[CV] gamma=0.187983142488, C=23.3461384887 ...........................\n",
      "[CV] ............ gamma=0.187983142488, C=23.3461384887, total=   1.3s\n",
      "[CV] gamma=0.00682053403264, C=90.5343597549 .........................\n",
      "[CV] .......... gamma=0.00682053403264, C=90.5343597549, total=   1.4s\n",
      "[CV] gamma=0.00682053403264, C=90.5343597549 .........................\n",
      "[CV] .......... gamma=0.00682053403264, C=90.5343597549, total=   2.1s\n",
      "[CV] gamma=0.00682053403264, C=90.5343597549 .........................\n",
      "[CV] .......... gamma=0.00682053403264, C=90.5343597549, total=   1.7s\n",
      "[CV] gamma=0.0788340051027, C=43.3523245358 ..........................\n",
      "[CV] ........... gamma=0.0788340051027, C=43.3523245358, total=   1.8s\n",
      "[CV] gamma=0.0788340051027, C=43.3523245358 ..........................\n",
      "[CV] ........... gamma=0.0788340051027, C=43.3523245358, total=   1.7s\n",
      "[CV] gamma=0.0788340051027, C=43.3523245358 ..........................\n",
      "[CV] ........... gamma=0.0788340051027, C=43.3523245358, total=   1.4s\n",
      "[CV] gamma=0.00369752066141, C=69.8437383453 .........................\n",
      "[CV] .......... gamma=0.00369752066141, C=69.8437383453, total=   1.3s\n",
      "[CV] gamma=0.00369752066141, C=69.8437383453 .........................\n",
      "[CV] .......... gamma=0.00369752066141, C=69.8437383453, total=   1.5s\n",
      "[CV] gamma=0.00369752066141, C=69.8437383453 .........................\n",
      "[CV] .......... gamma=0.00369752066141, C=69.8437383453, total=   1.3s\n",
      "[CV] gamma=0.0105317121384, C=83.4630171004 ..........................\n",
      "[CV] ........... gamma=0.0105317121384, C=83.4630171004, total=   1.4s\n",
      "[CV] gamma=0.0105317121384, C=83.4630171004 ..........................\n",
      "[CV] ........... gamma=0.0105317121384, C=83.4630171004, total=   1.6s\n",
      "[CV] gamma=0.0105317121384, C=83.4630171004 ..........................\n",
      "[CV] ........... gamma=0.0105317121384, C=83.4630171004, total=   1.7s\n",
      "[CV] gamma=0.107001440334, C=49.4138004108 ...........................\n",
      "[CV] ............ gamma=0.107001440334, C=49.4138004108, total=   1.4s\n",
      "[CV] gamma=0.107001440334, C=49.4138004108 ...........................\n",
      "[CV] ............ gamma=0.107001440334, C=49.4138004108, total=   1.6s\n",
      "[CV] gamma=0.107001440334, C=49.4138004108 ...........................\n",
      "[CV] ............ gamma=0.107001440334, C=49.4138004108, total=   1.5s\n",
      "[CV] gamma=0.00220670990664, C=77.0683478825 .........................\n",
      "[CV] .......... gamma=0.00220670990664, C=77.0683478825, total=   1.1s\n",
      "[CV] gamma=0.00220670990664, C=77.0683478825 .........................\n",
      "[CV] .......... gamma=0.00220670990664, C=77.0683478825, total=   1.5s\n",
      "[CV] gamma=0.00220670990664, C=77.0683478825 .........................\n",
      "[CV] .......... gamma=0.00220670990664, C=77.0683478825, total=   1.3s\n",
      "[CV] gamma=0.107947214541, C=38.6726922934 ...........................\n",
      "[CV] ............ gamma=0.107947214541, C=38.6726922934, total=   1.4s\n",
      "[CV] gamma=0.107947214541, C=38.6726922934 ...........................\n",
      "[CV] ............ gamma=0.107947214541, C=38.6726922934, total=   1.5s\n",
      "[CV] gamma=0.107947214541, C=38.6726922934 ...........................\n",
      "[CV] ............ gamma=0.107947214541, C=38.6726922934, total=   1.4s\n",
      "[CV] gamma=0.00707037127039, C=77.5272590644 .........................\n",
      "[CV] .......... gamma=0.00707037127039, C=77.5272590644, total=   1.2s\n",
      "[CV] gamma=0.00707037127039, C=77.5272590644 .........................\n",
      "[CV] .......... gamma=0.00707037127039, C=77.5272590644, total=   1.3s\n",
      "[CV] gamma=0.00707037127039, C=77.5272590644 .........................\n",
      "[CV] .......... gamma=0.00707037127039, C=77.5272590644, total=   1.7s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done  30 out of  30 | elapsed:  1.0min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=None, error_score='raise',\n",
       "          estimator=SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma='auto', kernel='rbf',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False),\n",
       "          fit_params={}, iid=True, n_iter=10, n_jobs=1,\n",
       "          param_distributions={'gamma': <scipy.stats._distn_infrastructure.rv_frozen object at 0x7f112d753b70>, 'C': <scipy.stats._distn_infrastructure.rv_frozen object at 0x7f112d8493c8>},\n",
       "          pre_dispatch='2*n_jobs', random_state=None, refit=True,\n",
       "          return_train_score=True, scoring=None, verbose=2)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from scipy.stats import reciprocal, uniform\n",
    "\n",
    "param_distributions = {\"gamma\": reciprocal(0.001, 0.1), \"C\": uniform(1, 10)}\n",
    "rnd_search_cv = RandomizedSearchCV(svm_clf, param_distributions, n_iter=10, verbose=2)\n",
    "rnd_search_cv.fit(X_train_scaled[:1000], y_train[:1000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=22.350452844489766, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma=0.0017707563307247142,\n",
       "  kernel='rbf', max_iter=-1, probability=False, random_state=None,\n",
       "  shrinking=True, tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rnd_search_cv.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.85599999999999998"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rnd_search_cv.best_score_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This looks pretty low but remember we only trained the model on 1,000 instances. Let's retrain the best estimator on the whole training set (run this at night, it will take hours):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=22.350452844489766, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma=0.0017707563307247142,\n",
       "  kernel='rbf', max_iter=-1, probability=False, random_state=None,\n",
       "  shrinking=True, tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rnd_search_cv.best_estimator_.fit(X_train_scaled, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.99996666666666667"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = rnd_search_cv.best_estimator_.predict(X_train_scaled)\n",
    "accuracy_score(y_train, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ah, this looks good! Let's select this model. Now we can test it on the test set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.97099999999999997"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = rnd_search_cv.best_estimator_.predict(X_test_scaled)\n",
    "accuracy_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Not too bad, but apparently the model is overfitting slightly. It's tempting to tweak the hyperparameters a bit more (e.g. decreasing `C` and/or `gamma`), but we would run the risk of overfitting the test set. Other people have found that the hyperparameters `C=5` and `gamma=0.005` yield even better performance (over 98% accuracy). By running the randomized search for longer and on a larger part of the training set, you may be able to find this as well."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 10."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_Exercise: train an SVM regressor on the California housing dataset._"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's load the dataset using Scikit-Learn's `fetch_california_housing()` function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.datasets import fetch_california_housing\n",
    "\n",
    "housing = fetch_california_housing()\n",
    "X = housing[\"data\"]\n",
    "y = housing[\"target\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Split it into a training set and a test set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Don't forget to scale the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "scaler = StandardScaler()\n",
    "X_train_scaled = scaler.fit_transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's train a simple `LinearSVR` first:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearSVR(C=1.0, dual=True, epsilon=0.0, fit_intercept=True,\n",
       "     intercept_scaling=1.0, loss='epsilon_insensitive', max_iter=1000,\n",
       "     random_state=None, tol=0.0001, verbose=0)"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import LinearSVR\n",
    "\n",
    "lin_svr = LinearSVR()\n",
    "lin_svr.fit(X_train_scaled, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see how it performs on the training set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.95277084531857559"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "y_pred = lin_svr.predict(X_train_scaled)\n",
    "mse = mean_squared_error(y_train, y_pred)\n",
    "mse"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at the RMSE:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9760998131946218"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sqrt(mse)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this training set, the targets are tens of thousands of dollars. The RMSE gives a rough idea of the kind of error you should expect (with a higher weight for large errors): so with this model we can expect errors somewhere around $10,000. Not great. Let's see if we can do better with an RBF Kernel. We will use randomized search with cross validation to find the appropriate hyperparameter values for `C` and `gamma`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 10 candidates, totalling 30 fits\n",
      "[CV] gamma=0.0192352268819, C=4.30764931019 ..........................\n",
      "[CV] ........... gamma=0.0192352268819, C=4.30764931019, total=  14.2s\n",
      "[CV] gamma=0.0192352268819, C=4.30764931019 ..........................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:   21.1s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] ........... gamma=0.0192352268819, C=4.30764931019, total=  15.4s\n",
      "[CV] gamma=0.0192352268819, C=4.30764931019 ..........................\n",
      "[CV] ........... gamma=0.0192352268819, C=4.30764931019, total=  15.2s\n",
      "[CV] gamma=0.0484815426092, C=6.73680442469 ..........................\n",
      "[CV] ........... gamma=0.0484815426092, C=6.73680442469, total=  16.3s\n",
      "[CV] gamma=0.0484815426092, C=6.73680442469 ..........................\n",
      "[CV] ........... gamma=0.0484815426092, C=6.73680442469, total=  17.4s\n",
      "[CV] gamma=0.0484815426092, C=6.73680442469 ..........................\n",
      "[CV] ........... gamma=0.0484815426092, C=6.73680442469, total=  14.9s\n",
      "[CV] gamma=0.017814491229, C=3.58273880952 ...........................\n",
      "[CV] ............ gamma=0.017814491229, C=3.58273880952, total=  14.4s\n",
      "[CV] gamma=0.017814491229, C=3.58273880952 ...........................\n",
      "[CV] ............ gamma=0.017814491229, C=3.58273880952, total=  14.4s\n",
      "[CV] gamma=0.017814491229, C=3.58273880952 ...........................\n",
      "[CV] ............ gamma=0.017814491229, C=3.58273880952, total=  14.5s\n",
      "[CV] gamma=0.0402484717134, C=7.82281513442 ..........................\n",
      "[CV] ........... gamma=0.0402484717134, C=7.82281513442, total=  17.0s\n",
      "[CV] gamma=0.0402484717134, C=7.82281513442 ..........................\n",
      "[CV] ........... gamma=0.0402484717134, C=7.82281513442, total=  15.6s\n",
      "[CV] gamma=0.0402484717134, C=7.82281513442 ..........................\n",
      "[CV] ........... gamma=0.0402484717134, C=7.82281513442, total=  17.8s\n",
      "[CV] gamma=0.0128939177358, C=2.27024131228 ..........................\n",
      "[CV] ........... gamma=0.0128939177358, C=2.27024131228, total=  17.3s\n",
      "[CV] gamma=0.0128939177358, C=2.27024131228 ..........................\n",
      "[CV] ........... gamma=0.0128939177358, C=2.27024131228, total=  14.7s\n",
      "[CV] gamma=0.0128939177358, C=2.27024131228 ..........................\n",
      "[CV] ........... gamma=0.0128939177358, C=2.27024131228, total=  13.9s\n",
      "[CV] gamma=0.0208072089102, C=5.80286867886 ..........................\n",
      "[CV] ........... gamma=0.0208072089102, C=5.80286867886, total=  16.5s\n",
      "[CV] gamma=0.0208072089102, C=5.80286867886 ..........................\n",
      "[CV] ........... gamma=0.0208072089102, C=5.80286867886, total=  16.4s\n",
      "[CV] gamma=0.0208072089102, C=5.80286867886 ..........................\n",
      "[CV] ........... gamma=0.0208072089102, C=5.80286867886, total=  14.4s\n",
      "[CV] gamma=0.00101498280765, C=9.96817670792 .........................\n",
      "[CV] .......... gamma=0.00101498280765, C=9.96817670792, total=  14.1s\n",
      "[CV] gamma=0.00101498280765, C=9.96817670792 .........................\n",
      "[CV] .......... gamma=0.00101498280765, C=9.96817670792, total=  16.3s\n",
      "[CV] gamma=0.00101498280765, C=9.96817670792 .........................\n",
      "[CV] .......... gamma=0.00101498280765, C=9.96817670792, total=  14.6s\n",
      "[CV] gamma=0.00576623827498, C=7.22063630977 .........................\n",
      "[CV] .......... gamma=0.00576623827498, C=7.22063630977, total=  15.4s\n",
      "[CV] gamma=0.00576623827498, C=7.22063630977 .........................\n",
      "[CV] .......... gamma=0.00576623827498, C=7.22063630977, total=  14.6s\n",
      "[CV] gamma=0.00576623827498, C=7.22063630977 .........................\n",
      "[CV] .......... gamma=0.00576623827498, C=7.22063630977, total=  16.8s\n",
      "[CV] gamma=0.00222917054008, C=4.62314937785 .........................\n",
      "[CV] .......... gamma=0.00222917054008, C=4.62314937785, total=  14.3s\n",
      "[CV] gamma=0.00222917054008, C=4.62314937785 .........................\n",
      "[CV] .......... gamma=0.00222917054008, C=4.62314937785, total=  14.5s\n",
      "[CV] gamma=0.00222917054008, C=4.62314937785 .........................\n",
      "[CV] .......... gamma=0.00222917054008, C=4.62314937785, total=  14.5s\n",
      "[CV] gamma=0.00233135007992, C=5.33346541687 .........................\n",
      "[CV] .......... gamma=0.00233135007992, C=5.33346541687, total=  13.2s\n",
      "[CV] gamma=0.00233135007992, C=5.33346541687 .........................\n",
      "[CV] .......... gamma=0.00233135007992, C=5.33346541687, total=  14.9s\n",
      "[CV] gamma=0.00233135007992, C=5.33346541687 .........................\n",
      "[CV] .......... gamma=0.00233135007992, C=5.33346541687, total=  15.6s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done  30 out of  30 | elapsed: 10.9min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=None, error_score='raise',\n",
       "          estimator=SVR(C=1.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, gamma='auto',\n",
       "  kernel='rbf', max_iter=-1, shrinking=True, tol=0.001, verbose=False),\n",
       "          fit_params={}, iid=True, n_iter=10, n_jobs=1,\n",
       "          param_distributions={'gamma': <scipy.stats._distn_infrastructure.rv_frozen object at 0x7f112d8595f8>, 'C': <scipy.stats._distn_infrastructure.rv_frozen object at 0x7f112d859e48>},\n",
       "          pre_dispatch='2*n_jobs', random_state=None, refit=True,\n",
       "          return_train_score=True, scoring=None, verbose=2)"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVR\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from scipy.stats import reciprocal, uniform\n",
    "\n",
    "param_distributions = {\"gamma\": reciprocal(0.001, 0.1), \"C\": uniform(1, 10)}\n",
    "rnd_search_cv = RandomizedSearchCV(SVR(), param_distributions, n_iter=10, verbose=2)\n",
    "rnd_search_cv.fit(X_train_scaled, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVR(C=6.7368044246881622, cache_size=200, coef0=0.0, degree=3, epsilon=0.1,\n",
       "  gamma=0.048481542609212967, kernel='rbf', max_iter=-1, shrinking=True,\n",
       "  tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rnd_search_cv.best_estimator_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's measure the RMSE on the training set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.58793661408914677"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = rnd_search_cv.best_estimator_.predict(X_train_scaled)\n",
    "mse = mean_squared_error(y_train, y_pred)\n",
    "np.sqrt(mse)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looks much better than the linear model. Let's select this model and evaluate it on the test set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.61198551879668228"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = rnd_search_cv.best_estimator_.predict(X_test_scaled)\n",
    "mse = mean_squared_error(y_test, y_pred)\n",
    "np.sqrt(mse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.3"
  },
  "nav_menu": {},
  "toc": {
   "navigate_menu": true,
   "number_sections": true,
   "sideBar": true,
   "threshold": 6,
   "toc_cell": false,
   "toc_section_display": "block",
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
